Miracle-dz/UniFAR-FTUs huggingface dataset:
Biodiversity Dynamics of Campylobacter Species in Chicken Tissues in Rural Households in Region Epirus, Greece

The Campylobacter species is considered as an emerging zoonotic threat to public health. C. jejuni and C. coli are the most studied species, yet a variety of other species of the same genus were found to be the causative agents of other diseases. Chicken meat has been described as an excellent vehicle for the transmission of some Campylobacter species but most of the relevant research has been conducted in urban populations and concerned meat of industrial-grade birds. To investigate the abundance and prevalence of the Campylobacter genus in rural free-grazing chicken, quantitative and qualitative methods at 37 and at 42 °C were employed. The possible correlation of the prevalence with certain epidemiological factors (size of the flock, presence of other poultry species, presence of small ruminants, feeding concentrates, or leftovers) has been also investigated. In total, 242–249 strains (depending on the method) belonging to the following 18 different Campylobacter species have been isolated: C. coli, C. rectus, C. hominis, C. helveticus, C. upsaliensis, C. jejuni, C. avium, C. fetus, C. hepaticus. , C. lari, C. sputorum, C. mucosalis, C. gracilis, C. showae, C. hyointestinalis, C. concisus, C. cuniculorum, and C. ureolyticus. The size of the flock and the presence of small ruminants in the same household were the most influential factors affecting the prevalence of most species. Campylobacter species biodiversity can be attributed to environmental, zoonotic, or anthropogenic contamination. Rural populations should be educated about the importance of self-protection measures during their contact with their poultry and the necessity to cook sufficiently the meat.
Weaponizing Cost of Quality of Food Manufacturers: Implications for Organizational Performance

Owing to the current business environment, food manufacturers must provide high-quality products while simultaneously providing competitive prices for their customers. Cost of quality (COQ) can play a crucial role in fostering organizational competitive performance through reduced costs. The aim of this paper is to analyze the interrelationship between the application of COQ and organizational performance in the context of Palestinian Food Manufacturing Organizations (PFMO). The study employs a questionnaire through which data is collected from 119 PFMO. The collected data is analyzed using the partial least square structural equation modeling (PLS-SEM) technique. The results of this study suggest that application of COQ practices has a strong effect on the organizational performance of PFMO. External, internal, and prevention costs were all found to have a positive impact on organizational performance of PFMO. However, appraisal cost has no impact on organizational performance. The study provides significant value to quality management literature on the role of COQ practices in enhancing performance of food manufacturers in a developing country context and responds to calls for enhancing organizational efficiency in today’s complex business environments.
Self-calibration of AGV inertial-odometric navigation using absolute-reference measurements

This paper describes the 'self calibration' algorithm and procedure for a navigation system suited for autonomous guided vehicles uses an inertial-odometric navigation system together with an absolute navigation system. In the algorithm proposed no external measurement systems or off-fine measurements are needed thus minimising the time and costs of the installation procedure. The method is explained by describing the navigation model and identifying the parameters that need to be calibrated The detailed algorithm that makes it possible to uncouple the different parameters, and thus maximising accuracy, is then shown. Finally, an experimental example using a mock-up of an industrial robot is given.
A note on integrating the Clapeyron equation without neglecting the specific fluid volume

Under certain approximations, the Clapeyron equation can be integrated to yield a simple exponential relation giving the saturation vapor pressure over a condensed phase as a function of temperature. The derivation usually assumes that the vapor behaves as an ideal gas with constant specific heat, and that the fluid also has constant specific heat. In addition, the specific fluid volume is neglected in comparison with the specific vapor volume. In this case, the Clapeyron equation is separable and readily integrable in closed form. It is shown here that this latter assumption is not required. A simple closed‐form relation between saturation vapor pressure and temperature is derived which includes the condensed phase‐specific volume. Two examples of the use of this result are presented.
Perspectives of carbon tax introduction in the Russian Federation

This paper analyses the prospects for the introduction of a carbon tax on the territory of the Russian Federation and the impact of the environmental agenda on the financial and tax policy of Russia. The authors consider the advantages and disadvantages of the carbon tax in comparison with other economic and legal mechanisms. The paper considers the definition of the concept of carbon tax and its essence, analyzes the role of carbon tax in the implementation of state policy in the field of environmental protection in terms of decarbonization. The authors also propose possible strategies for the introduction of a carbon tax in Russia.
Dual watermarking algorithm of colored images based on wavelet transform

A dual watermarking algorithm for colored images based on wavelet transform is proposed.The given color image is split into itsthreecomponents in the YIQ color space first,then the luminance component Y is chosen and decomposedby one levelDWT(discrete wavelet transform). Then some of its sub-images are selected to do so to choose the corporate position to embed the watermarks.The-oretical analysis and experiment results show that the watermark generated with the proposed algorithm is imperceptible and of great ro-bustness.
Nanomaterials‐Based Biosensors for the Detection of Prostate Cancer Biomarkers: Recent Trends and Future Perspective

Cancer is among the leading causes of death and an important barrier to improving life expectancy globally. Prostate cancer is the second most common cancer in men worldwide. The detection of biomarkers in body fluids is the key topic for the diagnosis and prognosis of prostate cancer. Despite advances in prostate cancer detection methods, therapeutic agents and new biomarkers, prostate cancer remains a serious challenge. Prostate‐specific antigen (PSA) is widely recognized as an important biomarker for the diagnosis of prostate cancer although researchers have also investigated the use of alternative biomarkers. Design and development of novel biosensors for prostate cancer detection has become a hot research area with advances in nanotechnology aiding biosensor development. This article reviews recent achievements and progress that nanomaterials and nanotechnology have made in biomarkers based biosensors for prostate cancer detection and covers: i) PSA‐targeted biosensors (immunosensors, aptamer‐based, peptide‐based and nanopore‐based biosensors), ii) sarcosine oxidase‐targeted biosensors, iii) other biomarkers based biosensors (prostate cancer antigen 3 (PCA3), vascular endothelial growth factor (VEGF) and prostate‐specific membrane antigen (PSMA)) including dual biomarkers based biosensors (PSA‐VEGF, PSA‐PSMA, PSA‐PCA3 and PSA‐sarcosine). The aim of this review is to provide insights into how nanomaterials in combination with various biomarkers are now aiding biosensor development in prostate cancer diagnostics.
Fractional differential operator for enhancing edge and texture of image

In order to improve the ability of enhancing image′s edge and texture,the fractional differential operator for frequent domain characteristics is proposed and analyzed.It shows that the fractional differential can greatly increase high frequency component,reinforce medium frequency component and non-linearly preserve low frequency component of the signal,hence they could be used for edge and texture enhancement.Starting from the definition of the fractional differential,an approximate fractional order differential module from which a linear filter is adopted is constructed.Image edge and texture have direction,and there is spatial correlation between the neighbored pixels,so there is the adjacent gray value.In this paper,an algorithm is proposed to improve the ability of fractional differential operator.Firstly,3×3 template is divided into four sub-templates with different directions around the detecting pixel,and then the four weight sum values for the four sub-templates are obtained.Consequently,an enhancing value which is maximum value of the four weight sum values is gained.The experiment results of edge enhancement show that the improve algorithm can enhance edge and texture information effectively more than 5×5 template operators.
Synthesis of Satellite and Surface Measurements, Model Results, and FRAPPÉ Study Findings to Assess the Impacts of Oil and Gas Emissions Reductions on Maximum Ozone in the Denver Metro and Northern Front Range Region in Colorado

The Denver Metro/Northern Front Range area has been designated a severe nonattainment area (NAA) for O3. Previous research associated with the 2014 Front Range Air Pollution and Photochemistry Éxperiment field campaign and a 2017 O3 source apportionment modeling analysis point to north‐south gradients in oil and gas (O&G) impacts on O3, with greater contributions to the north and a dominance of mobile sources to the south. Recent analysis of Atmospheric Infrared Sounder satellite methane enhancement and ethane trends in the area show ∼50% and ∼70% reductions in methane and ethane, respectively, from 2013 to 2020. These are consistent with reductions in O&G volatile organic compound (VOC) emissions estimated in the most recent O3 State Implementation Plan but inconsistent with a timeline of top‐down methane flux estimates for limited periods from 2012 to 2021. Reductions in annual fourth maximum eight‐hour O3 concentrations from 2012 to 2019 are 5 ppb greater at Fort Collins West (FTCW) to the north than at Chatfield Reservoir to the south. Bayesian correlation analyses and partial correlations between FTCW O3 and ethane conditioned for tropospheric NO2 from the Aura satellite Ozone Monitoring Instrument suggest that most of the 9‐ppb reduction here is due to declines in O&G emissions, and 9 ppb is comparable to estimated O&G VOC contributions on high‐concentration days in the northern portion of the study area. The gradient in trends suggests that significant O&G emissions reductions have benefited locations in the central and northern portions of the NAA.
An Exploration of Sources Fostering First-Year Engineering Students’ Academic Well-Being in a PBL Environment

Contribution: This article contributes to the literature surrounding first-year engineering students’ academic well-being by proposing a conceptual framework guiding an understanding of supportive sources that foster students’ academic well-being. A survey was designed and tested accordingly, and four factors that contribute to students’ academic well-being were identified to inspire the improvement of a future course and curriculum design. Background: Prior research has pointed out that students’ academic well-being has a significant impact on their persistence in their current study, learning experience, academic achievement, and competence development. However, limited studies have explored first-year engineering students’ academic well-being and supportive factors in the field of engineering higher education. To support engineering students to become agentic professionals, it is meaningful to pay close attention to their academic well-being and help them to become purposeful learners at an early stage of their professional development. Research Question: 1) How can an instrument be developed and validated to characterize the sources of students’ academic well-being in a project-based learning approaches (PBL) context? 2) What sources could foster students’ academic well-being in a PBL context? Are there significant differences in age, gender, and discipline as a function of sources of academic well-being? Methodology: With the guidance of the conceptual framework with the domains of internal and external sources, a survey was designed based on a literature review and conducted in a PBL environment. The survey’s content validity, construct validity, and reliability were tested using expert review, a pilot study, exploratory factor analysis (EFA), and Cronbach’s alpha. Findings: Supportive sources fostering students’ academic well-being were reported in the factors of personal values, agentic action, interactions within the learning environment, and external support. Comparisons between gender, age, and discipline verified the different impacts of the four factors on fostering academic well-being.
Tunable 3D Plasmonic Cavity Nanosensors for Surface-Enhanced Raman Spectroscopy with Sub-femtomolar Limit of Detection

Metallic nanohole arrays (NHAs) with a high hole density have emerged with potential applications for surface-enhanced Raman spectroscopy (SERS) including the detection of analytes at ultralow concentrations. However, these NHA structures generally yield weak localized surface plasmon resonance (LSPR), which is a prerequisite for SERS measurements. In this work, a compact three-dimensional (3D) tunable plasmonic cavity with extraordinary optical transmission properties serves as a molecular sensor with sub-femtomolar detection. The 3D nanosensor consists of a gold film containing an NHA with an underlying cavity and a gold nanocone array at the bottom of the cavity. These nanosensors provide remarkable surface plasmon polariton (SPP) and LSPR coupling, resulting in a significantly improved detection performance. The plasmonic tunability is evaluated both experimentally and theoretically. A SERS limit of detection of 10–16 M for 4-nitrothiophenol is obtained along with distribution mapping of the molecule .
Primary Ovarian Mucinous Adenocarcinoma, Expansile Type, Misperceived As Pregnancy by the Patient

We present the case of a woman in her 20s with an eight-month history of increasing abdominal distention, dyspnea, and night sweats. The patient believed she was pregnant despite being told at another hospital that the pregnancy tests were negative, and no fetus was seen on an abdominal ultrasound. The patient delayed obtaining follow-up because of a distrust of the healthcare system and presented to our hospital at the behest of her mother. On physical examination, the abdomen was distended with a positive fluid wave, and a large mass was palpated in the abdomen. Gynecological examination was limited because of severe abdominal distension but a mass was palpable in the right adnexa. A pregnancy test and fetal ultrasound were performed, and the patient was not pregnant. A CT scan of the abdomen and pelvis revealed a large mass arising from the right adnexa. She underwent right salpingo-oophorectomy, appendectomy, omentectomy, lymph node dissection, and peritoneal implant resection. The biopsy confirmed intestinal-type IIB primary ovarian mucinous adenocarcinoma, expansile type, with peritoneal spread. Chemotherapy was provided for three cycles. A follow-up CT scan of the abdomen showed no evidence of a tumor six months after surgery.
Aberrant Multimodal Connectivity Pattern Involved in Default Mode Network and Limbic Network in Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder that progressively affects bulbar and limb function. Despite increasing recognition of the disease as a multinetwork disorder characterized by aberrant structural and functional connectivity, its integrity agreement and its predictive value for disease diagnosis remain to be fully elucidated. In this study, we recruited 37 ALS patients and 25 healthy controls (HCs). High-resolution 3D T1-weighted imaging and resting-state functional magnetic resonance imaging were, respectively, applied to construct multimodal connectomes. Following strict neuroimaging selection criteria, 18 ALS and 25 HC patients were included. Network-based statistic (NBS) and the coupling of grey matter structural–functional connectivity (SC–FC coupling) were performed. Finally, the support vector machine (SVM) method was used to distinguish the ALS patients from HCs. Results showed that, compared with HCs, ALS individuals exhibited a significantly increased functional network, predominantly encompassing the connections between the default mode network (DMN) and the frontoparietal network (FPN). The increased structural connections predominantly involved the inter-regional connections between the limbic network (LN) and the DMN, the salience/ventral attention network (SVAN) and FPN, while the decreased structural connections mainly involved connections between the LN and the subcortical network (SN). We also found increased SC–FC coupling in DMN-related brain regions and decoupling in LN-related brain regions in ALS, which could differentiate ALS from HCs with promising capacity based on SVM. Our findings highlight that DMN and LN may play a vital role in the pathophysiological mechanism of ALS. Additionally, SC–FC coupling could be regarded as a promising neuroimaging biomarker for ALS and shows important clinical potential for early recognition of ALS individuals.
Detection of Basal Stem Rot Disease Using Deep Learning

Palm oil industry is an important economic resource for Malaysia. However, an oil palm tree disease called Basal Stem Rot has impeded the production of palm oil, which caused significant economic loss at the same time. The oil palm tree disease is caused by a fungus known as Ganoderma Boninense. Infected trees often have little to no symptoms during early stage of infection, which made early detection difficult. Early disease detection is necessary to allow early sanitization and disease control efforts. Using Terrestrial Laser Scanning technology, 88 grey-distribution canopy images of oil palm tree were obtained. The images were pre-processed and augmented before being used for training and testing of the deep learning models. The capabilities of the Convolution Neural Network deep learning models in the classification of dataset into healthy and non-healthy class were tested and the best performing model was identified based on the Macro-F1 score. Fine-tuned DenseNet121 model was the best performing model, recorded a Macro F1-score of 0.798. It was also noted that Baseline model showed a relatively remarkable macro-F1 score of 0.747, which was better than all the feature extractor models and some of the fine-tuned models. However, fine-tuned models suffered from model overfitting due to dataset limitations. For future work, it is recommended to increase the sample size and utilize other CNN architectures to improve the model performance and progress towards detecting Basal Stem Rot at the early stage of infection by classifying sample images into multiple classes.
Variations in the Spatial Distribution of Smart Parcel Lockers in the Central Metropolitan Region of Tianjin, China: A Comparative Analysis before and after COVID-19

The COVID-19 pandemic has led to a significant increase in e-commerce, which has prompted residents to shift their purchasing habits from offline to online. As a result, Smart Parcel Lockers (SPLs) have emerged as an accessible end-to-end delivery service that fits into the pandemic strategy of maintaining social distance and no-contact protocols. Although numerous studies have examined SPLs from various perspectives, few have analyzed their spatial distribution from an urban planning perspective, which could enhance the development of other disciplines in this field. To address this gap, we investigate the distribution of SPLs in Tianjin’s central urban area before and after the pandemic (i.e., 2019 and 2022) using kernel density estimation, average nearest neighbor analysis, standard deviation elliptic, and geographical detector. Our results show that, in three years, the number of SPLs has increased from 51 to 479, and a majority were installed in residential communities (i.e., 92.2% in 2019, and 97.7% in 2022). We find that SPLs were distributed randomly before the pandemic, but after the pandemic, SPLs agglomerated and followed Tianjin’s development pattern. We identify eight influential factors on the spatial distribution of SPLs and discuss their individual and compound effects. Our discussion highlights potential spatial distribution analysis, such as dynamic layout planning, to improve the allocation of SPLs in city planning and city logistics.
Apology Strategies Used by Prime Minister Kevin Rudd on National Apology “Stolen Generation” Speech

This research examines the use of apology strategies applied by Kevin Rudd in his speech of stolen generation. Kevin Rudd is an Australian former politician who was the 26th Prime Minister of Australian, serving from December 2007 to June 2010. This study is conducted to find out (1) the types of apology strategies Kevin Rudd used in his speech, and (2)the way on how apology strategies used by Kevin Rudd in his speech. This study applies descriptive qualitative method. The data were taken from the transcript of Kevin Rudd’s speech. This study uses Trosborg’s theory on apology strategies. The result of apology strategy analysis in Kevin Ruud’s speech shows that he has used the apology strategy in delivering his speech. This study reveals that there are seven categories and nine sub-categories of apology strategies on Kevin Rudd’s speech. The strategies are acknowledgement of responsibility, promise and forbearance, expressing concern for the hearer, minimizing the degree of offense, explanation or account, as well as offer of repair as the type of apology strategy which are employed. Based on Trosborg’s theory, the most dominant apology strategy used by Kevin Rudd in his speech was “expression of apology”.
The Thermochemical Conversion of Municipal Solid Waste by Torrefaction Process

In this work, the thermochemical properties of municipal solid waste (MSW) are studied using the torrefaction process as the main method for investigation. Torrefaction experiments were carried out using an electric laboratory furnace, at temperatures of 200, 250, and 300 °C. The residence time was set to 90 min. Proximate and ultimate analysis were performed on the torrefied MSW samples and compared with the properties of the raw MSW samples. In addition, the thermal properties of the obtained torrefied MSW samples were evaluated by thermogravimetric analysis (TGA) and derivative thermogravimetric analysis (DTG). The following could be stated: the obtained results showed that mass and energy yields (MY and EY, respectively) decrease with increasing when torrefaction temperature, while the heating values (HHV) increased under the same conditions (from 24.3 to 25.1 MJ/kg). Elemental analysis showed an increase in carbon content (C), from 45.7 ± 0.9 to 52.8 ± 1.05 wt. %, and decrease in oxygen content (O), from 45.6 ± 0.9 to 39.5 ± 0.8 wt. %, when torrefaction temperature is increased, which is consistent with the general definition of the torrefaction process. In addition, enhancement factors (EFs) and fuel ratios (FRs) were calculated, which ranged from 1.00 to 1.02 and 0.16 to 0.23, respectively. Some anomalies were observed during the thermal analysis, which are assumed to be related to the composition of the selected MSW. This study therefore shows that torrefaction pretreatment can improve the physicochemical properties of raw MSW to a level comparable to coal, and could contribute to a better understanding of the conversion of MSW into a valuable, solid biofuel.