bluuebunny/crossref_metadata_2025_split huggingface dataset:
Carrots or sticks? Inferring motives of corporate CSR Engagement from empirical data
Authors: Kotzian Peter 
Published: December 2022

AbstractWhat drives Corporate Social Responsibility (CSR) engagement of firms? Most empirical literature on firms’ CSR engagement focuses on benefits resulting from CSR engagement which are granted by the public. Other studies argue that firms are driven by fear of negative public reactions. This raises, firstly, the question, whether rewards or punishments drive CSR engagement of firms. Secondly, how can we find out, whether benefits or punishments are more relevant? This study starts out from the observation that CSR is defined by going beyond statutory prescriptions monitored and enforced by legal institutions. Existing literature argues that firms orient themselves at public expectations regarding CSR, which are presumed to increase over time. But the specific drivers and mechanisms of this increase are yet unclear. Based on simple behavioral assumptions, we derive developments of CSR engagement for the overall population of firms and sub-populations. These developments differ in crucial aspects, depending on whether firms engage in CSR because they expect benefits or punishments. Comparing empirical data on CSR engagement at the population and sub-group level from different economic regions with implications derived from the behavioral assumptions indicates that in the US, the public exercises pressure by imposing punishments rather than by offering rewards, while this is not the case in other economic regions.
Ultraviolet Applications to Control Patulin Produced by Penicillium expansum CMP-1 in Apple Products and Study of Further Patulin Degradation Products Formation and Toxicity
Authors: Nicolau-Lapeña Iolanda , Rodríguez-Bencomo Juan José , Colás-Medà Pilar , Viñas Inmaculada , Sanchis Vicente , Alegre Isabel 
Published: December 2022

Abstract
Patulin is a mycotoxin whose presence in apple-derived products and fruit juices is legally regulated, being its maximum limits established in the legislation of multiple countries. However, the management of contaminated batches is still an issue for producers. This investigation aims to evaluate ultraviolet light (254 nm, UV-C254nm) irradiation to find solutions that can be applied at different stages of the apple juice production chain. In this regard, 8.8 (UV-1) and 35.1 (UV-2) kJ m−2 treatments inactivated spores of Penicillium expansum CMP-1 on the surface of apples. Although the same treatments applied to wounded apples (either before the infection or after the infection, immediately or when the lesion had appeared) did not show any effect on the growth rate of P. expansum during storage (up to 14 days, at 4 or 25 °C), they reduced patulin content per lesion size in apples treated after the infection had occurred (patulin decreased from 2.24 (control) to 0.65 µg kg−1 cm−2 (UV-2 treated apples)). Additionally, the treatment of juice with patulin with ultraviolet light up to 450.6 kJ m−2 resulted in more than 98 % reduction of patulin. Degradation products of patulin after UV-C254nm treatments were tentatively identified by HPLC–MS, and toxicity and biological activities were assessed in silico, and results indicated that such products did not pose an increased risk when compared to patulin.
Reflections on Criminal Justice Reform: Challenges and Opportunities
Authors: Lattimore Pamela K. 
Published: December 2022

AbstractConsiderable efforts and resources have been expended to enact reforms to the criminal justice system over the last five decades. Concerns about dramatic increases in violent crime beginning in the late Sixties and accelerating into the 1980s led to the “War on Drugs” and the “War on Crime” that included implementation of more punitive policies and dramatic increases in incarceration and community supervision. More recent reform efforts have focused on strategies to reduce the negative impacts of policing, the disparate impacts of pretrial practices, and better strategies for reducing criminal behavior. Renewed interest in strategies and interventions to reduce criminal behavior has coincided with a focus on identifying “what works.” Recent increases in violence have shifted the national dialog from a focus on progressive reforms to reduce reliance on punitive measures and the disparate impact of the legal system on some groups to a focus on increased investment in “tough on crime” criminal justice approaches. This essay offers some reflections on the “Waged Wars” and the efforts to identify “What Works” based on nearly 40 years of work evaluating criminal justice reform efforts.
Prognostic value of semi-quantitative parameters of 18F-FDG PET/CT in newly diagnosed multiple myeloma patients
Authors: Wan Baoyu , Zhang Song , Wang Peng , Deng Pengyi , Dai Wenli 
Published: December 2022

Abstract
Objective
To investigate the prognostic value of fluroine-18 fluorodexyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) semi-quantitative parameter in newly diagnosed multiple myeloma (NDMM) and to design a new staging system including 18F-FDG PET/CT semi-quantitative parameters for NDMM.

Methods
A total of 38 NDMM patients who underwent 18F-FDG PET/CT examination in Yichang Central People’s Hospital from February 2014 to April 2021 were collected. The relationship between the characteristics of 18F-FDG PET/CT (metabolic tumor volume of all lesions (aMTV), total lesion glycolysis of all lesions (aTLG), maximum standardized uptake values (SUVmax) of the lesion with largest MTV (mSUVmax), extramedullary disease (EMD), focal lesions (FLs)), the laboratory parameters, and prognostic parameters (progression-free survival (PFS) and overall survival (OS)) were analyzed retrospectively. SPSS 25.0 statistical software was used for statistical processing, Kaplan–Meier method was used for survival analysis, Log-rank method was used for univariate analysis, and Cox proportional risk model was used for multivariate analysis.

Results
Univariate analysis showed that aMTV ≥ 90.97cm3, aTLG ≥ 283.31 g, hemoglobin (Hb) < 100 g/L, focal lesions (FLs) ≥ 10, (percentage of circulating plasma cells (CPC%) ≥ 30%, creatinine (Cr) ≥ 177umol/L, lactic dehydrogenase (LDH) ≥ 250 g/L might be the adverse prognostic factors of PFS in patients with NDMM, all p < 0.05; aMTV ≥ 90.97 cm3, aTLG ≥ 283.31 g, Hb < 100 g/L, FLs ≥ 10, mSUVmax ≥ 5.8, the presence of extramedullary disease (EMD) and PCPs ≥ 30% may be adverse prognostic factors for OS in patients with NDMM, all p < 0.05. Multivariate regression analysis showed that aMTV ≥ 90.97 cm3 was an independent risk factor for PFS in NDMM patients, p < 0.05; aMTV ≥ 90.97 cm3, mSUVmax ≥ 5.8, and the presence of EMD were independent risk factors for OS in the NDMM patients, all p < 0.05. According to the multivariate analysis results of OS, the New stage (NS) was performed. The 3-year OS rates of stage I, stage II, and stage III in NDMM patients were 100.0, 53.5, and 32.1%, respectively, p = 0.000.

Conclusion
aMTV can predict PFS and OS of NDMM patients better than other parameters. NS which combined with aMTV can predict OS of NDMM patients better and can provide an accurate and simple method for risk stratification of NDMM patients.
Protein–protein interaction prediction methods: from docking-based to AI-based approaches
Authors: Tsuchiya Yuko , Yamamori Yu , Tomii Kentaro 
Published: December 2022

AbstractProtein–protein interactions (PPIs), such as protein–protein inhibitor, antibody–antigen complex, and supercomplexes play diverse and important roles in cells. Recent advances in structural analysis methods, including cryo-EM, for the determination of protein complex structures are remarkable. Nevertheless, much room remains for improvement and utilization of computational methods to predict PPIs because of the large number and great diversity of unresolved complex structures. This review introduces a wide array of computational methods, including our own, for estimating PPIs including antibody–antigen interactions, offering both historical and forward-looking perspectives.
A simplified modeling procedure for adsorption at varying pH conditions using the modified Langmuir–Freundlich isotherm
Authors: Pereira Sharon K. , Kini Srinivas , Prabhu Balakrishna , Jeppu Gautham P. 
Published: December 2022

AbstractAnalytical isotherm models such as Langmuir isotherm, Freundlich isotherm, and other linear isotherms are commonly used for modeling adsorption datasets for a wide range of adsorption studies. Most of these studies consider pH to be fixed. However, pH is an important parameter that varies widely. Hence, the model parameters developed for one set of experiments cannot be used in another scenario where the pH is different. Surface complexation models that can simulate pH changes are complex, multi-parameter models that are difficult to use. The modified Langmuir–Freundlich (MLF) isotherm developed earlier by us could simulate pH-dependent adsorption on goethite-coated sands. However, it has only been tested for arsenic adsorption on goethite-coated sands. Therefore, chromium adsorption datasets were considered to extend this MLF isotherm for other metal ions. Two different adsorbents, viz. coconut root activated carbon (CoAC) and palm male flower activated carbon (PaAC), were selected for the adsorption modeling of Cr(VI) using the MLF isotherm model. An improved modeling strategy was developed for fitting the MLF isotherm, which required only a single pH versus adsorption dataset, instead of several isotherms at different pH values. The new methodology could simulate the pH-dependent adsorption satisfactorily for various experimental datasets. The maximum adsorption capacity was 88.64 (mg/g) and 100.1 (mg/g) for PaAC and CoAC, respectively. The affinity constant for this model (Ka) was found to be 0.007 (L/mg) for PaAC dataset and 0.0106(L/mg) and 0.004 (L/mg) for the CoAC dataset. The average R2 values of fitting were calculated and found to be 0.98 for PaAC and 0.85 for CoAC. The average root mean square error (RSME) of the fitting of the model was 0.07 (less than 10%). This modeling strategy required less experimental data and did not require advanced characterization studies. Therefore, this study indicates that the MLF isotherm can be extended to other contaminants and for different adsorbents to model the pH-dependent adsorption.
Application of augmented bat algorithm with artificial neural network in forecasting river inflow in Malaysia
Authors: Wee Wei Joe , Chong Kai Lun , Ahmed Ali Najah , Malek Marlinda Binti Abdul , Huang Yuk Feng , Sherif Mohsen , Elshafie Ahmed 
Published: December 2022

AbstractHydrologists rely extensively on anticipating river streamflow (SF) to monitor and regulate flood management and water demand for people. Only a few simulation systems, where previous techniques failed to anticipate SF data quickly, let alone cost-effectively, and took a long time to execute. The bat algorithm (BA), a meta-heuristic approach, was used in this study to optimize the weights and biases of the artificial neural network (ANN) model. The proposed hybrid work was validated in five different study areas in Malaysia. The statistical tests analysis of the preliminary results revealed that hybrid BA-ANN was superior to forecasting the SF at all five selected study areas, with average RMSE values of 0.103 m3/s for training and 0.143 m3/s for testing as compared to ANN standalone training and testing yielding 0.091 m3/s and 0.116 m3/s, respectively. This finding signifies that the implementation of BA into the ANN model resulted in a 20% improvement. In addition, with an R2 score of 0.951, the proposed model showed a better correlation than the 0.937 value of R2 of standard ANN. Nonetheless, while the proposed work outperformed the conventional ANN, the Taylor diagram, violin plot, relative error, and scatter plot findings confirmed the disparities in the proposed work’s performance throughout the research regions. The findings of these evaluations highlighted that the adaptability of the proposed works would need detailed investigation because its performance differed from case to case.
In vitro–in vivo correlation of drug release profiles from medicated contact lenses using an in vitro eye blink model
Authors: Pereira-da-Mota Ana F. , Vivero-Lopez Maria , Garg Piyush , Phan Chau-Minh , Concheiro Angel , Jones Lyndon , Alvarez-Lorenzo Carmen 
Published: December 2022

AbstractThere is still a paucity of information on how in vitro release profiles from drug-loaded contact lenses (CLs) recorded in 3D printed eye models correlate with in vivo profiles. This work aims to evaluate the release profiles of two drug-loaded CLs in a 3D in vitro eye blink model and compare the obtained results with the release in a vial and the drug levels in tear fluid previously obtained from an animal in vivo study. In vitro release in the eye model was tested at two different flow rates (5 and 10 µL/min) and a blink speed of 1 blink/10 s. Model CLs were loaded with two different drugs, hydrophilic pravastatin and hydrophobic resveratrol. The release of both drugs was more sustained and lower in the 3D eye model compared to the in vitro release in vials. Interestingly, both drugs presented similar release patterns in the eye model and in vivo, although the total amount of drugs released in the eye model was significantly lower, especially for resveratrol. Strong correlations between percentages of pravastatin released in the eye model and in vivo were found. These findings suggest that the current 3D printed eye blink model could be a useful tool to measure the release of ophthalmic drugs from medicated CLs. Nevertheless, physiological parameters such as the composition of the tear fluid and eyeball surface, tear flow rates, and temperature should be optimized in further studies.
Graphical abstract
Biogenic silver-doped mesoporous silica nanoparticles for multifunctional eco-designed textile printing
Authors: Adel Abeer M. , El-Shall Fatma N. , Diab Mohamed A. , Al-Shemy Mona T. 
Published: December 2022

AbstractThe current study concerns the valorization of waste rice hulls into value-added materials. To achieve this goal, first, the extraction route of mesoporous silica nanoparticles (SiO2NPs) from rice hulls using citric acid as a carbon template was compared with that prepared by conventional methods of incineration only. Both routes were performed at different temperatures of 600 and 700°C, and the fabricated SiO2NPs were studied by N2 physisorption isotherm, X-ray diffraction (XRD), Fourier transform infrared (FTIR), dynamic light scattering (DLS), and thermogravimetric (TG) analyses. The results show the influence of both routes and temperature on the porous structure, surface area, and charge of as-fabricated SiO2NPs. Furthermore, the physico-chemical features of as-prepared mesoporous SiO2NPs were utilized, in a facile and green way, to prepare silver NPs with high anti-microbial activity. The results were validated by the tools of UV-visible, FTIR, XRD, and XPS spectroscopy. Finally, the fabricated silver-doped mesoporous SiO2NPs were used as auxiliary additives through one-pot printing of different types of fabric (i.e., polyester (synthetic fabric), cotton (natural fabric), and cotton/polyester (blended fabric)). The EDX mapping of the pigment-printed fabrics with Ag-doped SiO2NPs exhibited the homogeneous distribution of Ag and Si atoms together with C and O throughout the fabric matrix. Interestingly, the pigment-printed fabrics with Ag-doped SiO2NPs demonstrated superior antibacterial activity, ultraviolet protection factor (UPF), and color characteristics in comparison to the conventional dyeing component.
Graphical Abstract
Clustering: an R library to facilitate the analysis and comparison of cluster algorithms
Authors: Martos Luis Alfonso Pérez , García-Vico Ángel Miguel , González Pedro , Carmona Cristóbal J. 
Published: December 2022

AbstractClustering is an unsupervised learning method that divides data into groups of similar features. Researchers use this technique to categorise and automatically classify unlabelled data to reveal data concentrations. Although there are other implementations of clustering algorithms in R, this paper introduces the Clustering library for R, aimed at facilitating the analysis and comparison between clustering algorithms. Specifically, the library uses relevant clustering algorithms from the literature with two objectives: firstly to group data homogeneously by establishing differences between clusters and secondly to generate a ranking between the algorithms and the attributes of a data set to obtain the optimal number of clusters. Finally, it is crucial to highlight the added value that the library provides through its interactive graphical user interface, where experiments can be easily configured and executed without requiring expert knowledge of the parameters of each algorithm.
Ultrasound in the diagnosis and differential diagnosis of enoral and plunging ranula: a detailed and comparative analysis
Authors: Koch Michael , Mantsopoulos Konstantinos , Leibl Victoria , Müller Sarina , Iro Heinrich , Sievert Matti 
Published: December 2022

AbstractPurposeTo develop sonographic criteria for ranula that to allow rapid and precise diagnosis, differentiation between enoral (ER) and plunging ranula (PR), and differential diagnosis from other competing pathologies in this region.MethodsPatients who presented with or were referred with ranula between 2002 and 2022 were assessed in a retrospective study. After clinical investigation, ultrasound examinations were performed in all cases. Several sonographic parameters describing the echotexture, shape and size of ranulas, their relationship to important surrounding anatomical landmarks and the characteristic spreading pattern of ERs and PRs were elaborated and evaluated.Results207 ranulas were included (82.12% ERs and 17.87% PRs). The ranulas were all in close anatomical relationship to the sublingual gland (SLG) and mylohyoid muscle (MM). The echo texture was hypoechoic to anechoic in 97.6% of the lesions. In comparison with ERs, PRs were larger and irregular in shape significantly more often (P = 0.0001). There were significant differences between ERs and PRs in their exact location relative to the SLG (superficial, deep, anterior, eachP = 0.0001; posterior,P = 0.03) and level of the MM (above, below, above and below,P = 0.0001 each). The exact extent and plunging pattern were depicted in all PRs, but naturally in none of the ERs.ConclusionsThe ultrasound criteria developed in this study, confirming previously published results, indicate that ultrasound is an excellent diagnostic tool for diagnosing ranula and differentiating between ERs and PRs.
The Utility of Machine Learning Models for Predicting Chemical Contaminants in Drinking Water: Promise, Challenges, and Opportunities
Authors: Hu Xindi C. , Dai Mona , Sun Jennifer M. , Sunderland Elsie M. 
Published: December 2022

Abstract

Purpose of Review
This review aims to better understand the utility of machine learning algorithms for predicting spatial patterns of contaminants in the United States (U.S.) drinking water.

Recent Findings
We found 27 U.S. drinking water studies in the past ten years that used machine learning algorithms to predict water quality. Most studies (42%) developed random forest classification models for groundwater. Continuous models show low predictive power, suggesting that larger datasets and additional predictors are needed. Categorical/classification models for arsenic and nitrate that predict exceedances of pollution thresholds are most common in the literature because of good national scale data coverage and priority as environmental health concerns. Most groundwater data used to develop models were obtained from the United States Geological Survey (USGS) National Water Information System (NWIS). Predictors were similar across contaminants but challenges are posed by the lack of a standard methodology for imputation, pre-processing, and differing availability of data across regions.

Summary
We reviewed 27 articles that focused on seven drinking water contaminants. Good performance metrics were reported for binary models that classified chemical concentrations above a threshold value by finding significant predictors. Classification models are especially useful for assisting in the design of sampling efforts by identifying high-risk areas. Only a few studies have developed continuous models and obtaining good predictive performance for such models is still challenging. Improving continuous models is important for potential future use in epidemiological studies to supplement data gaps in exposure assessments for drinking water contaminants. While significant progress has been made over the past decade, methodological advances are still needed for selecting appropriate model performance metrics and accounting for spatial autocorrelations in data. Finally, improved infrastructure for code and data sharing would spearhead more rapid advances in machine-learning models for drinking water quality.
Spatial estimation of groundwater quality, hydrogeochemical investigation, and health impacts of shallow groundwater in Kabul city, Afghanistan
Authors: Hamidi Mohammad Daud , Kissane Stephen , Bogush Anna A. , Karim Abdul Qayeum , Sagintayev Janay , Towers Sam , Greenwell Hugh Christopher 
Published: December 2022

AbstractThe management of groundwater in densely populated areas with no centralized water treatment is critical for the prevention of diseases and maintaining sanitation. Here, we determine the bacteriological and chemical characteristics of groundwater in Kabul city, a resource that 4.1 million individuals rely on. Groundwater samples were analyzed from 41 newly established piezometric wells across Kabul, and data were compared with the last detailed study, undertaken in 2007, to understand contamination trends in an area that has undergone significant development and social changes. Piper diagrams, Gibbs diagrams, correlation analysis, and bivariate plots examine the hydrogeochemical and natural occurring processes of groundwater. The average concentration of cations followed the order Na+  > Mg2+  > Ca2+  > K+, and anions HCO3− > NO3− > Cl− > SO42− > F with Gibbs diagrams indicating mainly rock-weathering influence groundwater chemistry. An increase in nitrate (NO3−) and E. coli indicates anthropogenic activities impacting the shallow groundwater quality, with significantly elevated nitrate (over 50 mg/L) and E. coli (up to 250 CFU/100 mL). The increasing presence of E. coli and NO3− in the shallow groundwater of Kabul city in turn suggests problematic links to the prevalence of waterborne diseases. Additionally, the water quality index (WQI) was used to assess groundwater quality, and rank its suitability for drinking purposes. The WQI analysis showed that less than 35% of shallow groundwater samples had good water quality. The findings of this study are crucial for the development and sustainable management of groundwater in the city. In short term, we propose interventions such as point-of-use (POU) water purification which may offer temporary respite for waterborne disease prevention. Kabul city requires immediate attention to developing sustainable groundwater management policies, expansion of the water supply network, groundwater quality monitoring, and wastewater management.