Important Data Research Articles

Cognitive Functional Therapy compared with usual physiotherapy care in people with persistent low back pain: a mixed methods feasibility randomised controlled trial in the United Kingdom National Health Service

ObjectivesTo determine the feasibility of completing a definitive randomised controlled trial (RCT), evaluating the clinical and cost-effectiveness of Cognitive Functional Therapy (CFT) in comparison to usual physiotherapy care (UPC), for people with persistent low back pain (LBP). Design and settingA two-arm parallel feasibility RCT completed in a United Kingdom (UK) Secondary Care National Health Service (NHS) physiotherapy service. ParticipantsSixty adult participants who reported LBP lasting for more than three months, that was not attributable to a serious (e.g. cancer) or specific (e.g. radiculopathy) underlying cause, were invited to participate. Participants were allocated at random to receive CFT or UPC. InterventionsCognitive Functional Therapy and Usual Physiotherapy Care for persistent LBP. Main outcome measuresThe primary outcome was the feasibility of completing a definitive RCT, defined by recruitment of at least 5 participants per month, delivery of CFT per protocol and securing relevant and acceptable outcome measures. Data concerning study processes, resources, management and participant reported outcome measures were collected at baseline, 3, 6 and 12-month follow-up. ResultsSixty participants (n = 30 CFT and n = 30 UPC) were recruited with 80% (n = 48), 72% (n = 43) and 53% (n = 32) retained at 3, 6 and 12-month follow-up respectively. NHS physiotherapists were trained to competence and delivered CFT with fidelity. CFT was tolerated by participants with no adverse events. Relevant and clinically important outcome data were collected at all time points (0.4%, 3%, 1% and 0.8% of data was missing from the returned outcome measure booklets at baseline and 3, 6 and 12-month follow-up respectively). The Roland-Morris disability questionnaire was considered the most suitable primary outcome measure with a proposed sample size of 540 participants for a definitive cluster RCT. ConclusionIt is feasible to conduct a randomised study of CFT in comparison to UPC for NHS patients. A future study should incorporate an internal pilot to address aspects of feasibility further, including participant retention strategies. Clinical trial registration numberISRCTN12965286 Contribution of the paper•Cognitive Functional Therapy (CFT) has shown encouraging results for reducing pain and disability in people with persistent LBP in previous clinical trials. The clinical and cost-effectiveness of CFT has not previously been evaluated in a definitive trial in the United Kingdom National Health Service. This is the first study to evaluate the feasibility of completing such a trial.•The findings of this study confirmed that;•NHS patients can be recruited on time and to target, with no safety concerns regarding a randomised controlled trial.•NHS physiotherapists can be trained to competence to deliver CFT with fidelity.•Clinically relevant and important data can be collected using standardised outcome measures.•New knowledge about the feasibility of completing a definitive trial of CFT in the UK NHS has been gained.

Factors affecting prognosis in myelodysplastic syndrome: an 11 years’ experience from a tertiary care center

Aims: Myelodysplastic syndrome (MDS) is a clonal bone marrow neoplasia characterized by morphological findings of dysplasia in hematopoietic cells, peripheral cytopenia(s), ineffective hematopoiesis, recurrent genetic abnormalities, and an increased risk of conversion to acute myeloid leukemia (AML). The International Prognostic Scoring System (IPSS) is the most commonly used prognostic classification system for MDS. Classification was made by a combination of morphology, cytopenia, and genetic studies. In this study, we aimed to examine the parameters that affect prognosis in MDS patients, show their effects on mortality, and evaluate their positive or negative effects on the course of the disease. Methods: Two hundred twenty-nine patients who applied to Erciyes University Faculty of Medicine, Department of Hematology, and were diagnosed with MDS according to WHO classification between 2010 and 2020 were included in this retrospective study. Age, gender, additional disease status, laboratory parameters, bone marrow biopsy materials, and genetic mutation analysis data were available. The bone marrow aspiration and biopsy examinations of each patient were evaluated and categorized according to the WHO classification. The prognosis was evaluated according to the data of the patients, survival-exitus, and survival after MDS-AML conversion. Risk scoring was analyzed with three different scoring systems (IPSS, WPSS, and R-IPSS). Results: Of the 229 MDS patients included in the study, 57% (131) were male. The mean age of the patients was 67 years. Age, MDS-AML conversion times, disease duration, cellularity, and pathology blast rate were found to be statistically significant between the groups (p<0.05). Leukocyte, neutrophil, platelet, hematocrit, lymphocyte, monocyte, CRP, erythropoietin, ferritin, and LDH data were found to be statistically significant regarding survival (p<0.05). Age, IPSS risk status 3, and W-PSS risk status 3 were found to be independent risk factors affecting survival. Conclusion: Age, IPSS high risk, and WPSS high risk status were found to be independent risk factors affecting survival. Although our study revealed important data in the analysis of MDS patients, single-center analysis of patients and retrospective analysis revealed the need for further studies.

Lessons on food security from the COVID-19 pandemic in Bermuda.

Compared with other OECD countries, Bermuda ranks third globally in terms of income inequality globally. During the COVID-19 pandemic, anecdotal evidence suggested, significant fluctuations in the food demand and supply. We aimed to examine the impact of the COVID-19 pandemic on food insecurity, with a focus on the availability and affordability of various foods in Bermuda. We utilized a cross-sectional study design to investigate potential drivers of food insecurity within the local population. To gauge the level of household food insecurity we relied on the Bermuda Omnibus survey (N = 400) undertaken by Total Research Associates Ltd via telephone. To assess changes in food availability and affordability we conducted semi-structured interviews with key stakeholders who played pivotal roles in shaping food accessibility availability and affordability of food in Bermuda. These interviews were systematically analysed using the framework method. We performed analyses of food retail and import data to evaluate fluctuations in food prices and their impact on food availability and affordability. We found statistically significant associations between changes in food consumption, household income, and government aid. Food aid beneficiaries ate fewer fruits and vegetables by 50% [95% CI:17%-83%] and less fresh meat and fish by 39% [95 CI:3%-75%] compared with residents who did not receive any aid during the COVID-19 period from March 2020 to March 2021. Although we did not identify statistically significant food price increases feeding programmes played a pivotal role in preventing food insecurity during the pandemic in Bermuda. However, a lack of monitoring regarding the nutritional quality within the programmes, allowed a wide availability of foods high in sugar, salts, and fats, disproportionately affected low-income populations. In conclusion, food availability in Bermuda remained largely unaffected during the pandemic. Nevertheless, the surge in demand for feeding programs underscores underlying food security challenges in Bermuda and warrants further attention.

Authentication Performance Analysis of Acoustic-Based Ear Biometrics System Using Audible Signals

A study of an acoustic-based ear biometrics system for individual identification using audible signals was undertaken. In this era of too much network connectivity, an increase in cybercrimes has led to the loss of key information or leakage of it. Experts have therefore continued to work hard to protect and secure important data from being stolen, lost, leaked, or tempered with. The use of biometric systems is one of the methods that many have adopted to provide the needed security and protection of data. The authors in this study proposed an acoustics-based ear biometric system for the identification of individuals while taking into consideration the dynamics of real-time data capture and using real persons for user authentication applications. The system has been developed in the MATLAB/SIMULINK language which supports dynamic real-time data capture. The results of simulation experiments showed that with proper experimentation and threshold calibration, it is possible to develop acoustics-based authentication systems that can identify individuals correctly and with 100% recognition accuracy. Depending on the human subjects under study, the threshold cosine similarity setting may vary between 0.2 and 0.4. However, this variation is offset by the enrollment procedure deployed in practice. Furthermore, trend analysis using moving average analysis revealed the possibility that a false acceptance is equally likely even though 100% recognition accuracy was attained.

A study on the thermodynamic characteristics of a mechanical A/C swing angle milling head

Several studies have evaluated the thermal error modeling and structural characteristics of the swing angle milling head; however, few studies have reported the thermodynamic characteristics of the swing angle milling head. Therefore, based on the thermodynamic theory, the thermodynamic characteristics of the mechanical A/C swing angle milling head of the five axis CNC gantry milling machine are evaluated, and the relevant heating calculation results are obtained. The temperature field distribution and thermodynamic characteristics of the swing angle milling head were simulated and analyzed by using ANSYS Workbench finite element software. In addition, the accuracy and rationality of the finite element simulation results of this study are verified based on the experimental conclusions of other relevant scholars that the maximum temperature of the gear is also mainly distributed near the pitch line of the tooth surface. The research results indicate that the maximum heat generation area inside the swing angle milling head is mainly concentrated on the pitch line of the bevel gear tooth surface of the main spindle execution component, with a maximum temperature of 43.559°C. The maximum thermal deformation is located at the tip of the swing angle milling head, with a maximum thermal deformation of 1.8353 × 10−5 m, this study can provide important theoretical reference and data support for the structural optimization and thermal deformation control of subsequent swing angle milling heads.

Mouse Data Attack Technique Using Machine Learning in Image-Based User Authentication: Based on a Defense Technique Using the WM_INPUT Message

Recently, as the non-face-to-face society persists due to the coronavirus (COVID-19), the Internet usage rate continues to increase, and input devices, such as keyboards and mice, are mainly used to authenticate users in non-face-to-face environments. Due to the nature of the non-face-to-face environment, important personal data are processed, and since these personal data include authentication information, it is very important to protect them. As such, personal information, including authentication information, is entered mainly from the keyboard, and attackers use attack tools, such as keyloggers, to steal keyboard data in order to grab sensitive user information. Therefore, to prevent disclosure of sensitive keyboard input, various image-based user authentication technologies have emerged that allow sensitive information, such as authentication information, to be entered via mouse. To address mouse data stealing vulnerabilities via GetCursorPos() function or WM_INPUT message, which are representative mouse data attack techniques, a mouse data defense technique has emerged that prevents attackers from classifying real mouse data and fake mouse data by the defender generating fake mouse data. In this paper, we propose a mouse data attack technique using machine learning against a mouse data defense technique using the WM_INPUT message. The proposed technique uses machine learning models to classify fake mouse data and real mouse data in a scenario where the mouse data defense technique, utilizing the WM_INPUT message in image-based user authentication, is deployed. This approach is verified through experiments designed to assess its effectiveness in preventing the theft of real mouse data, which constitute the user’s authentication information. For verification purposes, a mouse data attack system was configured, and datasets for machine learning were established by collecting mouse data from the configured attack system. To enhance the performance of machine learning classification, evaluations were conducted based on data organized according to various machine learning models, datasets, features, and generation cycles. The results, highlighting the highest performance in terms of features and datasets were derived. If the mouse data attack technique proposed in this paper is used, attackers can potentially steal the user’s authentication information from various websites or services, including software, systems, and servers that rely on authentication information. It is anticipated that attackers may exploit the stolen authentication information for additional damages, such as voice phishing. In the future, we plan to conduct research on defense techniques aimed at securely protecting mouse data, even if the mouse data attack technique proposed in this paper is attempted.

Real-World evidence revelations: The potential of patient support programmes to provide data on medication usage.

Patient Support Programmes (PSPs) are used by the pharmaceutical industry to provide education and support to consumers to overcome the challenges they face managing their condition and treatment. Whilst there is an increasing number of PSPs, limited information is available on whether these programmes contribute to safety signals. PSPs do not have a scientific hypothesis, nor are they governed by a protocol. However, by their nature, PSPs inevitably generate adverse event (AE) reports. The main goal of the research was to gather all Novartis-initiated PSPs for sacubitril/valsartan, followed by research in the company safety database to identify all AE reports emanating from these PSPs. Core data sheets (CDS) were reviewed to assess if these PSPs contributed to any new, regulatory-authority approved, validated signals. Overall, AEs entered into the safety database from PSPs confirmed no contribution to CDS updates. Detailed review of real-world data revealed tablet splitting or taking one higher dose tablet a day instead of twice daily. This research, and subsequent analyses, revealed that PSPs did not impact safety label changes for sacubitril/valsartan. It revealed an important finding concerning drug utilisation i.e. splitting of sacubitril/valsartan tablets to reduce cost. This finding suggests that PSPs may contribute important real-world data on patterns of medication usage. There remains a paucity of literature available on this topic, hence further research is required to assess if it would be worth designing PSPs for collecting data on drug utilisation and (lack of) efficacy. Such information from PSPs could be important for all stakeholders.

Data-centric artificial olfactory system based on the eigengraph

Recent studies of electronic nose system tend to waste significant amount of important data in odor identification. Until now, the sensitivity-oriented data composition has made it difficult to discover meaningful data to apply artificial intelligence in terms of in-depth analysis for odor attributes specifying the identities of gas molecules, ultimately resulting in hindering the advancement of the artificial olfactory technology. Here, we realize a data-centric approach to implement standardized artificial olfactory systems inspired by human olfactory mechanisms by formally defining and utilizing the concept of Eigengraph in electrochemisty. The implicit odor attributes of the eigengraphs were mathematically substantialized as the Fourier transform-based Mel-Frequency Cepstral Coefficient feature vectors. Their effectiveness and applicability in deep learning processes for gas classification have been clearly demonstrated through experiments on complex mixed gases and automobile exhaust gases. We suggest that our findings can be widely applied as source technologies to develop standardized artificial olfactory systems.

A NATIONWIDE MULTICENTRE STUDY EVALUATING THE PREVALENCE, CASEMIX, AND MANAGEMENT OF PERIPROSTHETIC FEMUR FRACTURES: THE SCOTTISH NATIONAL AUDIT OF PERIPROSTHETIC FEMUR FRACTURES (SNAP FEMUR) INITIAL REPORT

Periprosthetic femur fracture (PPF) are heterogeneous, complex, and thought to be increasingly prevalent. The aims were to evaluate PPF prevalence, casemix, management, and outcomes.This nationwide study included all PPF patients aged >50 years from 16 Scottish hospitals in 2019. Variables included: demographics; implant and fracture factors; management factors, and outcomes.There were 332 patients, mean age 79.5 years, and 220/332 (66.3%) were female. One-third (37.3%) were ASA1-2 and two-thirds (62.3%) were ASA3+, 91.0% were from home/sheltered housing, and median Clinical Frailty Score was 4.0 (IQR 3.0). Acute medical issues featured in 87/332 (26.2%) and 19/332 (5.7%) had associated injuries. There were 251/332 (75.6%) associated with a proximal femoral implant, of which 232/251 (92.4%) were arthroplasty devices (194/251 [77.3%] total hip, 35/251 [13.9%] hemiarthroplasty, 3/251 [1.2%] resurfacing). There were 81/332 (24.4%) associated with a distal femoral implant (76/81 [93.8%] were total knee arthroplasties). In 38/332 (11.4%) there were implants proximally and distally. Most patients (268/332; 80.7%) were treated surgically, with 174/268 (64.9%) requiring fixation only and 104/268 (38.8%) requiring an arthroplasty or combined solution. Median time to theatre was longer for arthroplasty versus fixation procedures (120 vs 46 hours), and those requiring inter-hospital transfer waited longer (94 vs 48 hours).Barriers to investigating PPF include varied classification, coding challenges, and limitations of existing registries. This is the first study to examine a national PPF cohort and presents important data to guide service design and research. Additional findings relating to fracture patterns, implant types, surgeon skill-mix, and outcomes are reported herein.

Using PIT tags to infer bat reproductive status and parturition date: busy nights during lactation

Abstract Passive integrated transponders (PIT tags) can aid in the collection of important demographic data for species for which other methods, such as GPS technology, are not suitable. PIT tags can be particularly suitable to monitor small and cryptic species like bats and permit inference on their behavioral ecology. Literature for several species of bats states that females change their nightly activity patterns—going out and in of the roost only once per night during gestation compared to several times during lactation. Hence, we tested whether PIT tag detection patterns could be used to infer reproductive status and parturition date of female bats. From 2017 to 2021, we recorded detections of PIT-tagged little brown (Myotis lucifugus) and northern long-eared bats (M. septentrionalis) at the entrance of 2 maternity roosts in Québec and Newfoundland, Canada. We also used the maternity roost in Québec as a case study to further evaluate the potential of this method to link behavior and demography and investigated factors affecting parturition date. We were able to infer reproductive status for 63% to 97% of tagged individuals detected during both the gestation and the lactation periods, and parturition date for 61% to 95% of reproductive individuals, depending on the year and roost. Early spring arrival at the roost and warm spring mean temperature at night were associated with earlier parturition dates. Herein, we highlight that PIT tag systems may be useful to detect changes in activity patterns of female bats and infer individual reproductive parameters, which is on the long-term less stressful for bats and easier for researchers. We demonstrate that this approach is useful to investigate intrinsic and extrinsic factors of reproductive parameters, improving our understanding of bat population dynamics resulting in more informed population management decisions.

ECONOMIC POTENTIAL ANALYSIS RENGKAM ON BATAM ISLAND

: The marine and fisheries resources sector is one of the potentials that is widely utilized by coastal communities who mostly work as fishermen. The lives of fishermen who depend on natural conditions can be proven by the income of fishermen who increase during the fish season. The off-season causes the intensity of fishing to decrease, thus the amount of income received by fishermen is certainly drastically reduced. Collecting rengkam can be a solution for fishermen who have obstacles in catching fish. This profession switch provides benefits and improves welfare and makes coastal communities have choices to make a living. Rengkam has the potential to improve the welfare of coastal communities, especially during the season when fish are difficult to obtain, while it has consequences for ecosystem changes that cannot be ruled out. This study aims to scientifically examine the social and economic potential of rengkam exploration and review the environmental impacts of exploration activities and determine strategies that can be carried out to maximize social and economic potential and minimize the possibility of environmental impacts. This research uses a qualitative approach. Qualitative approach was conducted through in-depth interviews and field observations (primary data collection). This research was conducted at one location of the rengkam exploration center, Pecong Island, with the subjects involved in the research process being the community and SKPD at the research location. Measurements using survey instruments, interviews, observations and literature review. Qualitative data collected was analyzed using the Narrative Analysis method of important data not only related to context, but also time, place, specifications, experience, and suggestions for consideration.

Nurses' experiences of using a computer-based triage decision support system in the emergency department.

Emergency department triage is a vital and complex decision-making process. There is limited knowledge about nurses' experiences with triage decision support systems in emergency departments. This study was conducted to examine nurses' experiences with a computer-based triage decision support system in the emergency department. This is a qualitative and phenomenological study. Data were collected through interviews from 14 triage nurses who used a triage decision support system in the emergency department of a university hospital. The nurses were recruited for semi-structured interviews. All interviews were recorded on a voice recorder and then transcribed. Data were analysed with the inductive content analysis method. The interviewer asked comprehensive questions about the nurses' experiences with the triage decision support system. Three main and 11 sub-themes were elicited as a result of the analysis of the in-depth interviews: (a) the facilitating the triage decision theme, which included help in case of a dilemma, team collaboration, monitoring/supervision, and error reduction sub-themes; (b) the contribution to professionalism theme, which included ease of learning and teaching triage, professional autonomy, creating a database, and evidence-based practice sub-themes; (c) the areas that need improvement theme, which included reducing screen clicks, the effect of the hospital automation system performance, and clinical descriptors not included in the algorithm sub-themes. Triage nurses stated that the decision support system was beneficial and facilitated decision-making. The decision support system enabled triage nurses to make their own decisions using their clinical knowledge and experience, without a restriction on their professional autonomy, and this was perceived positively. It was emphasized that this system could be a support tool in educating nurses new to triage. However, nurses stated that the pace of the decision support system was affected by the performance of the hospital automation system and that the large number of steps used for electing items caused a waste of time. They also added that these were technical areas that needed improvement. The study provides important data that will help healthcare organizations and professionals better understand the emergency department nurse triage decision support system and gain a versatile, comprehensive, and general understanding.

Critical Assessment of Cocoa Classification with Limited Reference Data: A Study in Côte d’Ivoire and Ghana Using Sentinel-2 and Random Forest Model

Cocoa is the economic backbone of Côte d’Ivoire and Ghana, making them the leading cocoa-producing countries in the world. However, cocoa farming has been a major driver of deforestation and landscape degradation in West Africa. Various stakeholders are striving for a zero-deforestation cocoa sector by implementing sustainable farming strategies and a more transparent supply chain. In the context of tracking cocoa sources and contributing to cocoa-driven deforestation monitoring, the demand for accurate and up-to-date maps of cocoa plantations is increasing. Yet, access to limited reference data and imperfect data quality can impose challenges in producing reliable maps. This study classified full-sun-cocoa-growing areas using limited reference data relative to the large and heterogeneous study areas in Côte d’Ivoire and Ghana. A Sentinel-2 composite image of 2021 was generated to train a random forest model. We undertook reference data refinement, selection of the most important handcrafted features and data sampling to ensure spatial independence. After refining the quality of the reference data and despite their size reduction, the random forest performance was improved, achieving an overall accuracy of 85.1 ± 2.0% and an F1 score of 84.6 ± 2.4% (mean ± one standard deviation from ten bootstrapping iterations). Emphasis was given to the qualitative visual assessment of the map using very high-resolution images, which revealed cases of strong and weak generalisation capacity of the random forest. Further insight was gained from the comparative analysis of our map with two previous cocoa classification studies. Implications of the use of cocoa maps for reporting were discussed.

(364) Development of a Men’s Health Report Card for the State of Florida

Abstract Introduction Collecting regional health data that identifies causes of morbidity and mortality is imperative in order to guide the planning and implementation of programs and policies that will ultimately improve men’s health. Unintentional impact accidents are a major source of morbidity and mortality for young males across the United States. Other potentially reversible behaviors that have adverse behaviors that n’s health include smoking, vaping, and failing to undergo recommended cancer screenings. Corroborating this data on a regional level within our state of Florida, as well as understanding the prevalence of activities and behaviors that may adversely impact men’s health is important to build a strategy that reverses these trends. Objective The overarching goal was to develop a Men’s Health Report Card for the State of Florida which elucidated important health data that could be used to drive an evidence-based men’s health initiative for our state. In this study, we sought to evaluate the impact of accidental injuries on mortality and define specifics associated with those accidental injuries that may be used to mitigate the incidence of accidental deaths, Methods Data was obtained from various sources to develop a Men's Health Report Card for the State of Florida. The sources include the United States Census Bureau (USCB), the State Department of Health, the Center for Disease Control and Prevention (CDC) and the Florida Department of Health (FDH). The demographic data obtained from the USCB includes information on the population size, age distribution, race, and ethnicity breakdown within Florida. FDH contributed health indicator data from multiple sources. This data includes information from death certificates, providing insight into the leading causes of death for men in Florida, including COVID-19 death reports specific to gender and age. USCB American Community Survey provides data on social determinants of health, such as high school graduation rates and income levels falling below the federal poverty guidelines. CDC National Center for Health Statistics Mortality in the United States Report provides data on life expectancy in Florida and the United States organized by race and sex. The data spanned from the years 2015 and 2018. Results Figure 1 highlights some of the important information that was gleaned with regard to the state of Men’s Health in Florida. On average, Floridian men had a 5- year shorter life expectancy at birth than their female counterparts. Leading causes of male deaths in our state include heart disease, cancer, unintentional injuries, chronic lung disease, and cerebrovascular disease. The relative incidence of these sources of mortality is age dependent. It is notable that among young males, ages 18-54 years old, unintentional injuries, homicide, and suicide accounted for approximately 75% of male deaths. With respect to health behaviors: smoking and vaping, a sedentary lifestyle, obesity, alcohol abuse, and not wearing a seat belt remain highly prevalent among Floridian males. Notable among the cancer screenings, 30% of Floridian men over 50 years have never had a colonoscopy and 40% of men 50 years and older have never had a serum PSA test. Finally, it is notable that 15% of Floridian males could not see a physician due to cost, and close to 20% had no healthcare insurance coverage. Conclusions The State of Florida Men’s Health Report card defines significant causes of morbidity and mortality for Floridian males. As can be seen in this report, many of the sources of morbidity and mortality arise from unhealthy lifestyle choices, which if addressed could potentially reverse some of these trends. Racial, ethnic, and geographic health disparities among Floridian males exist and will require further work in the future to remedy this situation. Disclosure No.

Analysis of Experimental Measurements of Particulate Matter (PM) and Lung Deposition Surface Area (LDSA) in Operational Faces of an Oil Shale Underground Mine

Particulate matter (PM) in the context of underground mining results from various operations such as rock drilling and blasting, ore loading, hauling, crushing, dumping, and from diesel exhaust gases as well. These operations result in the formation of fine particles that can accumulate in the lungs of mineworkers. The lung deposited surface area (LDSA) concentration is a variant solution to evaluate potential health impacts. The aim of this study is to analyse PM and LDSA concentrations in the operational workings of the oil shale underground mine. Experimental measurements were carried out by a direct-reading real-time PM monitor, Dusttrak DRX, and a multimetric fine particle detector, Naneous Partector 2, during the loading and dumping processes using the diesel engine loader. Consequently, the analysis was conducted on PM, LDSA, particle surface area concentration (SA), average particle diameter (d), particle number concentration (PNC), and particle mass (PM0.3), producing a few valuable correlation factors. Averaged LDSA was around 1433 μm2/cm3 and reached maximum peaks of 2140 μm2/cm3 during the loading, which was mostly related to diesel exhaust emissions, and within the dumping 730 μm2/cm3 and 1840 μm2/cm3, respectively. At the same time, average PM1 was about 300 μg/ m3 during the loading, but within the dumping peaks, it reached up to 10,900 μg/ m3. During the loading phase, particle diameter ranged from 30 to 90 nm, while during the dumping phase peaks, it varied from 90 to 160 nm. On this basis, a relationship between PNC and particle diameter has been produced to demonstrate an approximate split between diesel particulate matter (DPM) and oil shale dust diameters. This study offers important data on PM and LDSA concentration that can be used for estimating potential exposure to miners at various working operations in the oil shale underground mines, and will be used for air quality control in accordance with establishing toxic aerosol health effects.

Study on the processes and warpage of double‐injection‐molded parts containing PP recycled materials

AbstractDouble‐injection molding involves the use of two injection units, A and B, which can combine two different plastics into one part in a single injection molding process. Double‐injection molding can promote the recycling of plastic waste as well as improve the performance and dull appearance of plastic products made from recycled materials. However, the warpage of parts is a significant challenge in double‐injection molding. Warpage is significantly affected by the properties of the recycled material and injection molding process. In this study, the relationship between the warpage of double‐injection‐molded parts using raw and recycled materials was studied through uniform design experiments. To investigate the processes and warpage, the material was recycled several times, and its properties were tested. A regression model was established to describe the quantitative relationship between the important parameters and warpage. The number‐average molecular weight and weight‐average molecular weight exhibited the largest decrease after the first recycling cycle with a 21.11% and 41.11% decrease, respectively. The melting temperature decreased from 164.61 to 159.35°C, and the crystallinity did not change significantly. The processing parameters varied with the change in the properties of PP recycled materials, with the melt temperature decreased from 225 to 210°C. In addition, the number of recycling cycles and holding time for B were the most important factors influencing the warpage of double‐injection‐molded parts, followed by holding time for A and melt temperature for A. Warpage can be reduced by increasing holding times B and A. The experimental results provide important data for reducing the warpage of double‐injection‐molded parts with recycled materials.Highlights Double‐injection‐molded parts containing recycled materials were prepared. Recycling enhanced the filling capacity of polypropylene. The regression model of warpage with variables was established. Warpage can be improved by adjusting processes and materials.

In Silico Studies on Natural Products and Derivatives Against Different Types of Cancer.

According to the World Health Organization (WHO), cancer is the second cause of death worldwide, responsible for almost 10 million deaths and accounting for one in every six deaths. It is a disease that can affect any organ or tissue with rapid progression to the final stage, which is metastasis, in which the disease spreads to different regions of the body. Many studies have been carried out to find a cure for cancer. Early diagnosis contributes to the individual achieving the cure; however, deaths are increasing considerably due to late diagnosis. Thus, this bibliographical review discussed several scientific research works pointing to in silico analyses in the proposition of new antineoplastic agents for glioblastoma, breast, colon, prostate, and lung cancer, as well as some of their respective molecular receptors involved in molecular docking simulations and molecular dynamics. This review involved articles describing the contribution of computational techniques for the development of new drugs or already existing drugs with biological activity; thus, important data were highlighted in each study, such as the techniques used, results obtained in each study, and the conclusion. Furthermore, 3D chemical structures of the molecules with the best computational response and significant interactions between the tested molecules and the PDB receptors were also presented. With this, it is expected to help new research in the fight against cancer, the creation of new antitumor drugs, and the advancement of the pharmaceutical industry and scientific knowledge about studied tumors.

Firearm manufacturing and imports in the USA and their association to firearm homicides in Central America and the Caribbean, 1991–2019

BackgroundFirearm manufacturing and imports grew in the US during the mid-2000s. We hypothesise those increases corresponded to increased international firearms trafficking and in turn were associated with increases in firearm...

A Constrained Competitive Swarm Optimizer With an SVM-Based Surrogate Model for Feature Selection

Feature selection (FS) is an important data preprocessing technique that selects a small subset of relevant features to improve learning performance. However, it is also challenging due to its large search space. Recently, competitive swarm optimiser (CSO) has shown promising results in FS because of its potential global search ability. The main idea of CSO is to select two solutions randomly and then let the loser (worse fitness) learn from the winner (better fitness). Although such a search mechanism provides a high population diversity, it is at risk of generating unqualified solutions since the winner’s quality is not guaranteed. In this work, we propose a constrained evolutionary mechanism for CSO, which verifies the quality of all the particles and lets the infeasible (unqualified) solutions learn from the feasible (qualified) ones. We also propose a novel local search and a size-change operator that guide the population to search for smaller feature subsets with similar or better classification performance. A surrogate model, based on support vector machines, is proposed to assist both local search and the size-change operator to explore a massive number of potential feature subsets without requiring excessive computational resource. Results on 24 real-world datasets show that the proposed algorithm can select smaller feature subsets with higher classification performance than state-of-the-art evolutionary computation (EC) and non-EC benchmark algorithms.

Recent Developments and Challenges in Projecting the Impact of Crop Productivity Growth on Biodiversity Considering Market-Mediated Effects.

The effect of an increase in crop productivity (output per unit of inputs) on biodiversity is hitherto poorly understood. This is because increased productivity of a crop in particular regions leads to increased profit that can encourage expansion of its cultivated area causing land use change and ultimately biodiversity loss, a phenomenon also known as "Jevons paradox" or the "rebound effect". Modeling such consequences in an interconnected and globalized world considering such rebound effects is challenging. Here, we discuss the use of computable general equilibrium (CGE) and other economic models in combination with ecological models to project consequences of crop productivity improvements for biodiversity globally. While these economic models have the advantage of taking into account market-mediated responses, resource constraints, endogenous price responses, and dynamic bilateral patterns of trade, there remain a number of important research and data gaps in these models which must be addressed to improve their performance in assessment of the link between local crop productivity changes and global biodiversity. To this end, we call for breaking the silos and building interdisciplinary networks across the globe to facilitate data sharing and knowledge exchange in order to improve global-to-local-to-global analysis of land, biodiversity, and ecosystem sustainability.