bluuebunny/crossref_metadata_2025 huggingface dataset:
Cannabis Hunger Games: nutrient stress induction in flowering stage – impact of organic and mineral fertilizer levels on biomass, cannabidiol (CBD) yield and nutrient use efficiency
Authors: Massuela Danilo Crispim , Munz Sebastian , Hartung Jens , Nkebiwe Peteh Mehdi , Graeff-Hönninger Simone
Published: September 2023

Indoor medicinal cannabis cultivation systems enable year-round cultivation and better control of growing factors, however, such systems are energy and resource intensive. Nutrient deprivation during flowering can trigger nutrient translocation and modulate the production of cannabinoids, which might increase agronomic nutrient use efficiency, and thus, a more sustainable use of fertilizers. This experiment compares two fertilizer types (mineral and organic) applied in three dilutions (80, 160 and 240 mg N L−1) to evaluate the effect of nutrient deprivation during flowering on biomass, Cannabidiol (CBD) yield and nutrient use efficiency of N, P and K. This is the first study showing the potential to reduce fertilizer input while maintaining CBD yield of medicinal cannabis. Under nutrient stress, inflorescence yield was significantly lower at the final harvest, however, this was compensated by a higher CBD concentration, resulting in 95% of CBD yield using one-third less fertilizer. The higher nutrient use efficiency of N, P, and K in nutrient-deprived plants was achieved by a larger mobilization and translocation of nutrients increasing the utilization efficiency of acquired nutrients. The agronomic nutrient use efficiency of CBD yield – for N and K – increased 34% for the organic fertilizers and 72% for the mineral fertilizers comparing the dilution with one-third less nutrients (160) with the highest nutrient concentration (240). Differences in CBD yield between fertilizer types occurred only at the final harvest indicating limitations in nutrient uptake due to nutrient forms in the organic fertilizer. Our results showed a lower acquisition and utilization efficiency for the organic fertilizer, proposing the necessity to improve either the timing of bio-availability of organic fertilizers or the use of soil amendments.
Functional trait divergence associated with heteromorphic leaves in a climbing fig
Authors: Deng Jun-Yin , Wang Yong-Jin , Chen Lu-Fan , Luo Tong , Wang Rong , Chen Xiao-Yong
Published: September 2023

Introduction

Plants that display heteroblasty possess conspicuous variations in leaf morphology between their juvenile and adult phases, with certain species retaining juvenile-like leaves even in adulthood. Nevertheless, the ecological advantages of maintaining two or more distinct leaf types in heteroblastic plants at the adult stage remain unclear.

Method

The aim of this study is to examine the adaptive significance of heteroblastic leaves sampled from branches with divergent functions (sterile and fertile branches) of mature Ficus pumila individuals by comparing their morphological, anatomical, and physiological characteristics.

Result

Leaves on sterile branches (LSs) exhibited a significantly larger specific leaf area, thinner palisade and spongy tissues, lower chlorophyll contents, and lower light saturation points than leaves on fertile branches (LFs). These results demonstrate that LSs are better adapted to low light environments, while LFs are well equipped to take advantages of high light conditions. However, both LFs and LSs have a low light compensation point with no significant difference between them, indicating that they start to accumulate photosynthetic products under similar light conditions. Interestingly, significant higher net photosynthetic rate was detected in LFs, showing they have higher photosynthetic capacity. Furthermore, LFs produced significant more nutrients compared to LSs, which may associate to their ability of accumulating more photosynthetic products under full light conditions and higher photosynthetic capacity.

Discussion

Overall, we observed a pattern of divergence in morphological features of leaves on two functional branches. Anatomical and physiological features indicate that LFs have an advantage in varied light conditions, providing amounts of photosynthetic products to support the sexual reproduction, while LSs adapt to low light environments. Our findings provide evidence that heteroblasty facilitates F. pumila to utilize varying light environments, likely associated with its growth form as a climbing plant. This strategy allows the plant to allocate resources more effectively and optimize its overall fitness.
The development and initial feasibility testing of D-HOMES: a behavioral activation-based intervention for diabetes medication adherence and psychological wellness among people experiencing homelessness
Authors: Vickery Katherine Diaz , Ford Becky R. , Gelberg Lillian , Bonilla Zobeida , Strother Ella , Gust Susan , Adair Edward , Montori Victor M. , Linzer Mark , Evans Michael D. , Connett John , Heisler Michele , O'Connor Patrick J. , Busch Andrew M.
Published: September 2023

Introduction

Compared to stably housed peers, people experiencing homelessness (PEH) have lower rates of ideal glycemic control, and experience premature morbidity and mortality. High rates of behavioral health comorbidities and trauma add to access barriers driving poor outcomes. Limited evidence guides behavioral approaches to support the needs of PEH with diabetes. Lay coaching models can improve care for low-resource populations with diabetes, yet we found no evidence of programs specifically tailored to the needs of PEH.

Methods

We used a multistep, iterative process following the ORBIT model to develop the Diabetes Homeless Medication Support (D-HOMES) program, a new lifestyle intervention for PEH with type 2 diabetes. We built a community-engaged research team who participated in all of the following steps of treatment development: (1) initial treatment conceptualization drawing from evidence-based programs, (2) qualitative interviews with affected people and multi-disciplinary housing and healthcare providers, and (3) an open trial of D-HOMES to evaluate acceptability (Client Satisfaction Questionnaire, exit interview) and treatment engagement (completion rate of up to 10 offered coaching sessions).

Results

In step (1), the D-HOMES treatment manual drew from existing behavioral activation and lay health coach programs for diabetes as well as clinical resources from Health Care for the Homeless. Step (2) qualitative interviews (n = 26 patients, n = 21 providers) shaped counseling approaches, language and choices regarding interventionists, tools, and resources. PTSD symptoms were reported in 69% of patients. Step (3) trial participants (N = 10) overall found the program acceptable, however, we saw better program satisfaction and treatment engagement among more stably housed people. We developed adapted treatment materials for the target population and refined recruitment/retention strategies and trial procedures sensitive to prevalent discrimination and racism to better retain people of color and those with less stable housing.

Discussion

The research team has used these findings to inform an NIH-funded randomized control pilot trial. We found synergy between community-engaged research and the ORBIT model of behavioral treatment development to develop a new intervention designed for PEH with type 2 diabetes and address health equity gaps in people who have experienced trauma. We conclude that more work and different approaches are needed to address the needs of participants with the least stable housing.
Protocol for randomized personalized trial for stress management compared to standard of care
Authors: Goodwin Ashley M. , Miller Danielle , D’Angelo Stefani , Perrin Alexandra , Wiener Ruby , Greene Brittney , Romain Anne-Marie N. , Arader Lindsay , Chandereng Thevaa , Kuen Cheung Ying , Davidson Karina W. , Butler Mark
Published: September 2023

Stress is a significant public health burden in the United States, with most Americans reporting unhealthy levels of stress. Stress management techniques include various evidence-based treatments shown to be effective but with heterogeneous treatment responses, indicating a lack of uniform benefits for all individuals. Designed to assess a participant’s response to a specific intervention, personalized (N-of-1) trials provide guidance for which treatment (s) work (s) best for the individual. Prior studies examining the effects of mindfulness meditation, yoga, and walking for stress reduction found all three interventions to be associated with significant reductions in self-reported measures of stress. Delivering these treatments using a personalized trial approach has the potential to assist clinicians in identifying the best stress management techniques for individuals with persistently high stress while fostering treatment decisions that consider their personal condition/barriers. This trial will evaluate a personalized approach compared to standard of care for three interventions (guided mindfulness meditation; guided yoga; and guided brisk walking) to manage perceived stress. Participants will respond to daily surveys and wear a Fitbit device for 18 weeks. After a 2-week baseline period, participants in the personalized trial groups will receive 12 weeks of interventions in randomized order, while participants in the standard-of-care group will have access to all interventions for self-directed stress management. After intervention, all participants will undergo 2 weeks of observation, followed by two additional weeks of the stress management intervention of their choosing while continuing outcome measurement. At study completion, all participants will be sent a satisfaction survey. The primary analysis will compare perceived stress levels between the personalized and standard of care arms. The results of this trial will provide further support for the use of personalized designs for managing stress.

Clinical Trial Registration: clinicaltrials.gov, NCT05408832.

Protocol version: 9/14/2022, 21-0968-MRB.
Brazilian Jiu Jitsu players’ motivations to train
Authors: Tarver Terrance L. , Levy Jacob J.
Published: September 2023

Combat sports, such as Brazilian Jiu Jitsu (BJJ), require intense physical, mental, and emotional tasking within its training. With the degree of difficulty ingrained within the sport, many participants that once were intrigued by the sport may lose this interest and enjoyment if their goals are not met. The purpose of this study was to examine the relative strength of sport motivations among BJJ players. Participants included 228 BJJ athletes varying in levels of sport participation experience. Grounded in Self-Determination Theory, participants were assessed on five motives for sport participation including: fitness, appearance, competence, social, and interest/enjoyment. Motives related to interest/enjoyment, competence, and fitness, were rated relatively higher; and appearance and social were rated relatively lower regarding participants’ motivation for BJJ participation. Analyses were also conducted related to athletes’ years of experience and competitive level of participation (i.e., hobbyist or non-competitor to those who compete on a regular basis) There was a significant effect of competence and interest/enjoyment motivators among competitive BJJ players, regardless of years of experience in the sport. Findings from this study could aid coaches, sport clinicians, and sport psychologists in working with BJJ players by focusing their training on the motivators that are most appealing to these athletes.
Hybrid morphological-convolutional neural networks for computer-aided diagnosis
Authors: Canales-Fiscal Martha Rebeca , Tamez-Peña José Gerardo
Published: September 2023

Training deep Convolutional Neural Networks (CNNs) presents challenges in terms of memory requirements and computational resources, often resulting in issues such as model overfitting and lack of generalization. These challenges can only be mitigated by using an excessive number of training images. However, medical image datasets commonly suffer from data scarcity due to the complexities involved in their acquisition, preparation, and curation. To address this issue, we propose a compact and hybrid machine learning architecture based on the Morphological and Convolutional Neural Network (MCNN), followed by a Random Forest classifier. Unlike deep CNN architectures, the MCNN was specifically designed to achieve effective performance with medical image datasets limited to a few hundred samples. It incorporates various morphological operations into a single layer and uses independent neural networks to extract information from each signal channel. The final classification is obtained by utilizing a Random Forest classifier on the outputs of the last neural network layer. We compare the classification performance of our proposed method with three popular deep CNN architectures (ResNet-18, ShuffleNet-V2, and MobileNet-V2) using two training approaches: full training and transfer learning. The evaluation was conducted on two distinct medical image datasets: the ISIC dataset for melanoma classification and the ORIGA dataset for glaucoma classification. Results demonstrate that the MCNN method exhibits reliable performance in melanoma classification, achieving an AUC of 0.94 (95% CI: 0.91 to 0.97), outperforming the popular CNN architectures. For the glaucoma dataset, the MCNN achieved an AUC of 0.65 (95% CI: 0.53 to 0.74), which was similar to the performance of the popular CNN architectures. This study contributes to the understanding of mathematical morphology in shallow neural networks for medical image classification and highlights the potential of hybrid architectures in effectively learning from medical image datasets that are limited by a small number of case samples.
Hyperacute assessment of vertigo in suspected stroke
Authors: Morrow Stacy , Koohi Nehzat , Kaski Diego
Published: September 2023

The management of patients with acute vertigo is most challenging in the hyperacute phase, both due to the complexity of vertigo as a symptom, the range of possible causes, and the lack of training in neuro-otology for non-specialists. Perhaps of greatest relevance is differentiating between peripheral (usually benign, e.g., inner ear) causes and central (potentially more sinister, e.g., stroke) causes. Several diagnostic algorithms have been introduced to help detect stroke in patients with acute vertigo. However, these algorithms have been largely validated in patients with an acute vestibular syndrome (with nystagmus) for whom symptoms have been present for a minimum of 24 h. The most challenging period within the diagnostic process is the hyperacute phase that determines triage and treatment, but where none of the established algorithms have been validated. In this review, we specifically describe practical implementation considerations for evaluating patients with hyperacute vertigo, including the timing of diagnostic testing within the emergency department pathway, resource availability, and pitfalls associated with current practices.
Farmers in the transition toward sustainability: what is the role of their entrepreneurial identity?
Authors: Rossi Eleonora Sofia , Materia Valentina C. , Caracciolo Francesco , Blasi Emanuele , Pascucci Stefano
Published: September 2023

Introduction

The European Union has recently prompted a shift toward Ecological Intensification (EI) practices, aiming to harmonize agricultural productivity and environmental conservation. Despite the benefits of EI, its implementation has been limited, as farmers face challenges in business reorganization and supply chain adaptation. This paper investigates the role of contract farming (CF) in promoting the adoption of sustainable practices among Italian wheat producers. Specifically, it analyzes the influence of farmers’ entrepreneurial identity on their engagement in such initiatives.

Methods

Using the case study of Barilla Group’s Carta del Mulino initiative, an innovative contract farming scheme incentivizing sustainable EI practices, the study explores the relationship between entrepreneurial identity and participation in CF schemes supporting EI. Data from a sample of 314 soft wheat farmers in four regions of Northern Italy were collected to examine the role of entrepreneurial identity in the adoption of sustainable practices and participation in CF schemes. To evaluate the research hypotheses, two distinct econometric models were developed.

Results and discussion

The findings reveal that farmers with a more developed entrepreneurial identity are more likely to adopt more sustainable agricultural practices and engage in contractual schemes involving EI practices. The study highlights the importance of fostering and supporting farmers’ entrepreneurial identity while increasing their knowledge of alternative agricultural techniques to address the challenges of the agricultural sector. This integration of individual perspectives (entrepreneurial identity) with a systems view (contract farming schemes) offers valuable insights for future research, policy, and practice in agri-food systems sustainability.
Measurement and temporal and spatial characteristics of agricultural eco-efficiency under climate change: a case study of Anhui, China
Authors: Dong Hao , Han Jichang , Zhang Yang , Chen Tianqing , Sun Zenghui , Hou Xiandong , Liu Yu
Published: September 2023

Introduction

Agricultural eco-efficiency is an important index to evaluate the agricultural sustainable development and ecological economics, while simultaneously providing a metric for improvements to the rural environment and the stability of agricultural ecosystems.

Methods

This study took Anhui province as a case, and applied unit survey and list analysis methodologies to quantify rural agricultural non-point source pollution (NPS). Input-oriented super-efficient DEA-SBM was used to measure agricultural eco-efficiency in the typical North-South Transition Zone, and evaluated spatial correlations and differences.

Results

This study showed that NPS was relatively stable, with less than 5% local variation in Chemical Oxygen Demand (COD), Total Nitrogen (TN), and Total Phosphorus (TP) pollutants. The environmental pressure caused by livestock breeding and the use of chemical fertilizers was very substantial, and the differences of rural agricultural NPS in Anhui Province had obvious north-south characteristics. The agricultural eco-efficiency exhibited an “inverted N” trend. Affected by the “Spatial proximity effect” and the “Matthew effect”, it presented spatial agglomeration and positive spatial correlation. The regional differences were significant, and the heterogeneity increased in our study areas. The southern region had the greatest variation, followed by the northern region, with the smallest variation in the central region, although inter-regional differences were consistent.

Discussion

Though the rational allocation of resources, coordination between agricultural economic and environmental protection would be realized, and better conditions for the sustainable development of agricultural ecology and the long-term stability of agricultural ecosystem would be created.
Artificial intelligence planning and 3D printing augmented modules in the treatment of a complicated hip joint revision: a case report
Authors: Liu Yikai , Zhang Zian , Wang Wenzhe , Yu Chaoqun , Liu Chang , Huang Zhenchao , Xu Kaige , Zhang Haining
Published: September 2023

Total hip revision with osseous defects can be very difficult. Artificial intelligence offers preoperative planning, real-time measurement, and intraoperative judgment, which can guide prothesis placement more accurately. Three-dimensional printed metel augment modules which are made according to the individualized osseous anatomy, can fit the osseous defects well and provide mechanical support. In this case, we used AI to plan the size and position of the acetabular cup and 3D-printed augmented modules in a complicated hip revision with an acetabular bone defects, which achieved stable fixation and relieved hip pain postoperatively.
Barriers and opportunities for improving dog bite prevention and dog management practices in northern Indigenous communities
Authors: Daigle Laurence , Ravel André , Lévesque Francis , Mokoush Kabimbetas Noah , Rondenay Yves , Simon Audrey , Aenishaenslin Cécile
Published: September 2023

Globally, people living in northern Indigenous communities are at higher risk of dog bites than the rest of the population living in North America, with annual incidence ranging from 0.61 to 59.6/10,000 inhabitants. Considering that rabies is endemic in wild canid populations in certain regions of the Arctic, the prevention of dog bites and the management of dog populations are of crucial importance for public health in these contexts. Most northern communities lack access to veterinary services, mainly due to their remote geographical location and to limited financial resources. Currently, northern Indigenous communities are using different approaches and strategies to prevent dog bites and manage dog populations, but the effectiveness of these approaches sometimes lacks evidence, and their low acceptability may affect their implementation. This study aims to describe (1) the current access and uses of veterinary services, and (2) the perceived barriers and opportunities related to dog population management practices currently implemented, or that could be implemented, in a Naskapi community and an Innu community located in northern Quebec (Canada). Quantitative data were collected through a survey to inhabitants on veterinary services (n = 122). Qualitative data were collected using individual interviews to inhabitants and health professionals to describe how dog population management measures were perceived, and to identify barriers and opportunities related to their implementation (n = 37). Descriptive and inferential analysis (quantitative data) and thematic analysis (qualitative data) were performed. Results show that the two main measures implemented at the time of the study – dog culling and short-duration veterinary clinics – were not perceived as fully acceptable and sustainable. Reinforcing access to veterinary services and other dog-related services, such as shelters and training programs on dogs, was identified as a need to improve dog bites prevention and dog population management in remote Indigenous communities. The implementation of animal health measures should be decided by concerned Indigenous communities to follow decolonial practices. It includes ensuring informed consent of dog owners, improving communication before, during and after interventions, separating veterinary services from rehoming and, most importantly giving back to Indigenous communities the complete leadership over animal health in their communities.
Herd and animal level seroprevalence and associated risk factors of small ruminant brucellosis in the Korahey zone, Somali regional state, eastern Ethiopia
Authors: Hussen Ahmed Mohammed , Alemu Fikadu , Hasan Hussen Ahmed , Mohamed Abdimalik Hussein , Gebremeskel Haben Fesseha
Published: September 2023

Introduction

Brucellosis is a zoonosis of major public health and economic importance that is endemic in livestock in Ethiopia with varying levels of seroprevalence.

Methods

A cross-sectional study was carried out to determine the individual and herd-level seroprevalence of brucellosis in small ruminants in the Korahey zone of the Ethiopian Somali Region. A total of 324 sera from 63 herds of small ruminants were collected randomly using a multistage sampling technique and the sera were tested using the Rose Bengal Plate Test, and seropositive reactors were confirmed by the Complement Fixation Test.

Results and discussion

The seroprevalence of brucellosis at the herds and the individual level was 6.35% (95% CI: 0.0–13%) and 1.23% (95% CI: 0.0–2%), respectively; with 1.4% in goats and 0.9% in sheep. Moreover, predicted variables like age group, parity, history of abortion, fetal membranes, herd size, ownership of other livestock species, contact with wild animals in the past year, the introduction of new animals in the past year, and lending of breeding males in the past year were not significantly associated (p > 0.05) with Brucella seropositivity at individual and herd level seroprevalence during multivariable logistic regression analysis. Pastoral community awareness regarding the public health impact of brucellosis and the promotion of an intersectoral One Health approach for the effective control of brucellosis is recommended.
Carousel Greedy Algorithms for Feature Selection in Linear Regression
Authors: Wang Jiaqi , Golden Bruce , Cerrone Carmine
Published: September 2023

The carousel greedy algorithm (CG) was proposed several years ago as a generalized greedy algorithm. In this paper, we implement CG to solve linear regression problems with a cardinality constraint on the number of features. More specifically, we introduce a default version of CG that has several novel features. We compare its performance against stepwise regression and more sophisticated approaches using integer programming, and the results are encouraging. For example, CG consistently outperforms stepwise regression (from our preliminary experiments, we see that CG improves upon stepwise regression in 10 of 12 cases), but it is still computationally inexpensive. Furthermore, we show that the approach is applicable to several more general feature selection problems.
Design of a Lower Limb Exoskeleton: Robust Control, Simulation and Experimental Results
Authors: Salcido E. Anyuli Alvarez , Centeno-Barreda Daniel , Rosales Yukio , Lopéz-Gutiérrez Ricardo , Salazar Sergio , Lozano Rogelio
Published: September 2023

This paper presents the development of a robust control algorithm to be applied in a knee and ankle joint exoskeleton designed for rehabilitation of flexion/extension movements. The goal of the control law is to follow the trajectory of a straight leg extension routine in a sitting position. This routine is commonly used to rehabilitate an injury on an Anterior Cruciate Ligament (ACL) and it is applied to the knee and ankle joints. Moreover, the paper presents the development and implementation of the robotic structure of the ankle joint to integrate it into an exoskeleton for gait rehabilitation. The development of the dynamic model and the implementation of the control algorithm in simulation and experimental tests are presented, showing that the proposed control guarantees the convergence of the tracking error.
Improving Photosynthesis and Grain Yield in Wheat through Ridge–Furrow Ratio Optimization
Authors: Liu Kun , Shi Yu , Yu Zhenwen , Zhang Zhen , Zhang Yongli
Published: September 2023

The ridge–furrow planting pattern is an effective strategy to improve grain yield, and changes in the ridge and furrow microenvironments affect wheat yield. However, the mechanism by which wheat yields are increased at different ridge–furrow ratios is unclear. In this study, four planting modes, namely the traditional planting mode (M1) and ridge–furrow ratios of 50:50 cm (M2), 75:50 cm (M3), and 100:50 cm (M4), were established for wheat under field conditions from 2021 to 2023; the effects of different treatments on light energy utilization, dry matter accumulation and transport, and grain yield were studied. The findings demonstrated that the M3 treatment exhibited the highest enhancements in parameters such as leaf area index (LAI), canopy photosynthetically effective radiation interception rate, relative chlorophyll content (SPAD) index, and net photosynthetic rate. Moreover, the M3 treatment displayed superior grain filling compared to other treatments. The post-anthesis assimilate accumulation in the M3 treatment was 11.93%, 4.69%, and 13.13% higher than that of M1, M2, and M4 treatments, respectively, and the grain yield in M3 increased by 7.70–9.56%, 3.13–4.91%, and 8.69–10.90% compared with those in M1, M2, and M4 in the two growing seasons, respectively. In summary, under the conditions of this study, the M3 treatment led to higher LAI and SPAD values in flag leaves post-anthesis compared to the other treatments. Moreover, M3 optimized canopy structure, led to the highest canopy interception rate, and increased photosynthetic rates per individual plant. Consequently, there was a significant increase in post-anthesis dry matter accumulation, resulting in the highest grain yield achieved among the treatments.
Contribution of Arbuscular Mycorrhizal Fungi (AMF) in Improving the Growth and Yield Performances of Flax (Linum usitatissimum L.) to Salinity Stress
Authors: Kakabouki Ioanna , Stavropoulos Panteleimon , Roussis Ioannis , Mavroeidis Antonios , Bilalis Dimitrios
Published: September 2023

Throughout the world, salinity is a major environmental issue that limits agricultural productivity, particularly in arid and semi-arid regions. In addition, climate change is the most important reason for the salinization of agricultural soils in the world, so it is now essential to find solutions to increase salinity tolerance in plants. This study investigated the potential of arbuscular mycorrhizal fungi (AMF) inoculation to enhance the growth and yield performances of flax under different salinity levels by conducting a pot experiment. The experiment was laid out in a two-factor completely randomized design including AMF inoculation (AMF+: with inoculation; AMF−: without inoculation) and irrigation water salinity (0, 50, 100, and 150 mM NaCl). According to the results, it is evident that salt stress caused negative physiological effects, including limited growth, reduced photosynthesis, and decreased nitrogen (N) and phosphorus (P) content in the shoots and roots of flax plants. Moreover, mycorrhizal association improved the salt tolerance of the plants by increasing chlorophyll content, and enhancing N and P shoot and root contents and consequently yield parameters, such as seed and stem fiber yield, particularly at moderate salt concentrations (50 and 100 mM NaCl). In particular, under 100 mM, AMF increased the total chlorophyll content, N shoot and root content, P shoot and root content, and seed and stem fiber yield by 30.4%, 36.1%, 31.0%, 38.9%, 45.4%, 35.2%, and 26.9%, respectively. As a result of using AMF, flax plants grown under salt stress exhibited tolerance, suggesting that AMF could be applied in saline environments to maintain ecological stability.
Application of Machine Learning and Deep Learning Models in Prostate Cancer Diagnosis Using Medical Images: A Systematic Review
Authors: Olabanjo Olusola , Wusu Ashiribo , Asokere Mauton , Afisi Oseni , Okugbesan Basheerat , Olabanjo Olufemi , Folorunso Olusegun , Mazzara Manuel
Published: September 2023

Introduction: Prostate cancer (PCa) is one of the deadliest and most common causes of malignancy and death in men worldwide, with a higher prevalence and mortality in developing countries specifically. Factors such as age, family history, race and certain genetic mutations are some of the factors contributing to the occurrence of PCa in men. Recent advances in technology and algorithms gave rise to the computer-aided diagnosis (CAD) of PCa. With the availability of medical image datasets and emerging trends in state-of-the-art machine and deep learning techniques, there has been a growth in recent related publications. Materials and Methods: In this study, we present a systematic review of PCa diagnosis with medical images using machine learning and deep learning techniques. We conducted a thorough review of the relevant studies indexed in four databases (IEEE, PubMed, Springer and ScienceDirect) using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. With well-defined search terms, a total of 608 articles were identified, and 77 met the final inclusion criteria. The key elements in the included papers are presented and conclusions are drawn from them. Results: The findings show that the United States has the most research in PCa diagnosis with machine learning, Magnetic Resonance Images are the most used datasets and transfer learning is the most used method of diagnosing PCa in recent times. In addition, some available PCa datasets and some key considerations for the choice of loss function in the deep learning models are presented. The limitations and lessons learnt are discussed, and some key recommendations are made. Conclusion: The discoveries and the conclusions of this work are organized so as to enable researchers in the same domain to use this work and make crucial implementation decisions.
Use of a Local Anaesthetic/Antiseptic Formulation for the Treatment of Lambs Experimentally Infected with Orf Virus
Authors: Lacasta Delia , Ríos Marina , Ruiz de Arcaute Marta , Ortín Aurora , Ramos Juan José , Villanueva-Saz Sergio , Tejedor María Teresa , Ruiz Héctor , Borobia Marta , Reina Ramsés , Gómez Alex , Navarro Teresa , Windsor Peter Andrew
Published: September 2023

Contagious ecthyma is a highly transmissible eruptive viral disease of the skin and mucosa of sheep and goats distributed worldwide. The treatment of orf lesions is usually based on the use of antiseptics and antibiotics for the management of presumptive secondary infections, increasing risks of antimicrobial resistance. The wound dressing formulation Tri-Solfen® (TS) containing two local anaesthetics (lignocaine and bupivacaine), adrenaline and an antiseptic (cetrimide) in a gel formulation has been demonstrated to reduce suffering and enhance recovery in cattle and buffalo with oral and skin lesions due to foot-and-mouth disease virus infection and reduced the orf viral load in lambs. In the present study, experimental infection with the orf virus was conducted in 50 newborn lambs and 25 animals were treated after the presence of the first lesions with TS and repeated three days later. Daily clinical examination, haematological, serological, biomolecular and post-mortem analyses were conducted during 34 days after treatment. Results indicated that treatment had no effect on weight gain and clinical progression of the lesions. It was determined that seroconversion after experimental infection occurs 34 days after infection and suggested that the deep basal epithelial location of the orf lesions may have prevented the therapy from having altered the clinical course.
An Intrusion Detection Method Based on Hybrid Machine Learning and Neural Network in the Industrial Control Field
Authors: Sun Duo , Zhang Lei , Jin Kai , Ling Jiasheng , Zheng Xiaoyuan
Published: September 2023

Aiming at the imbalance of industrial control system data and the poor detection effect of industrial control intrusion detection systems on network attack traffic problems, we propose an ETM-TBD model based on hybrid machine learning and neural network models. Aiming at the problem of high dimensionality and imbalance in the amount of sample data in the massive data of industrial control systems, this paper proposes an IG-based feature selection method and an oversampling method for SMOTE. In the ETM-TBD model, we propose a hyperparameter optimization method based on Bayesian optimization used to optimize the parameters of the four basic machine learners in the model. By introducing a multi-head-attention mechanism, the Transformer module increases the attention between local features and global features, enabling the discovery of the internal relationship between features. Additionally, the BiGRU is used to preserve the temporal features of the dataset, while the DNN is used to extract deeper features. Finally, the SoftMax classifier is used to classify the output. By analyzing the results of the comparison and ablation experiments, it can be concluded that the F1-score of the ETM-TBD model on a robotic arm dataset is 0.9665 and the model has very low FNR and FPR scores of 0.0263 and 0.0081, respectively. It can be seen that the model in this paper is better than the traditional single machine learning algorithm as well as the algorithm lacking any of the modules.
Platooning Cooperative Adaptive Cruise Control for Dynamic Performance and Energy Saving: A Comparative Study of Linear Quadratic and Reinforcement Learning-Based Controllers
Authors: Borneo Angelo , Zerbato Luca , Miretti Federico , Tota Antonio , Galvagno Enrico , Misul Daniela Anna
Published: September 2023

In recent decades, the automotive industry has moved towards the development of advanced driver assistance systems to enhance the comfort, safety, and energy saving of road vehicles. The increasing connection and communication between vehicles (V2V) and infrastructure (V2I) enables further opportunities for their optimisation and allows for additional features. Among others, vehicle platooning is the coordinated control of a set of vehicles moving at a short distance, one behind the other, to minimise aerodynamic losses, and it represents a viable solution to reduce the energy consumption of freight transport. To achieve this aim, a new generation of adaptive cruise control is required, namely, cooperative adaptive cruise control (CACC). The present work aims to compare two CACC controllers applied to a platoon of heavy-duty electric trucks sharing the same linear spacing policy. A control technique based on reinforcement learning (RL) algorithm, with a deep deterministic policy gradient, and a classic linear quadratic control (LQC) are investigated. The comparative analysis of the two controllers evaluates the ability to track inter-vehicle distance and vehicle speed references during a standard driving cycle, the string stability, and the transient response when an unexpected obstacle occurs. Several performance indices (i.e., acceleration and jerk, battery state of charge, and energy consumption) are introduced as metrics to highlight the differences. By appropriately selecting the reward function of the RL algorithm, the analysed controllers achieve similar goals in terms of platoon dynamics, energy consumption, and string stability.
Exploring the Extension of Virtual Reality beyond the Five Senses: A Study on Human Aura Detection
Authors: Cheok Adrian David , Karolyi George
Published: September 2023

The realm of virtual reality, primarily dominated by visual experiences, stands at the cusp of a paradigm shift [...]
A New Optimization Design Method of Multi-Objective Indoor Air Supply Using the Kriging Model and NSGA-II
Authors: Guo Yu , Wang Yukun , Cao Yi , Long Zhengwei
Published: September 2023

When using meta-heuristic optimization approaches for optimization, a large number of samples are required. In particular, when generating a subgeneration, the utilization of existing samples is low and the number of individuals is high. Therefore, surrogate-based optimization has been developed, which greatly reduces the number of individuals in the subgeneration and the cost of optimization. In complex air supply scenarios, single-objective optimization results may not be comprehensive; therefore, this paper developed a double-objective air supply optimization method based on the Kriging surrogate model and Non-dominated Sorting Genetic Algorithms-II. And it proposed the infill criteria based on clustering to advance the Pareto Frontier. The method was validated with an inverse prediction case, and in particular, the problems when based on 3D steady-state simulations were analyzed. The results showed that the method can quickly achieve an approximate prediction of the boundary conditions (when predictions were made based on experimental data, the number of simulations was 82 and the average error was 6.8%). Finally, the method was used to optimize the air supply parameters of a dual-aisle, single-row cabin, with only 118 samples used in the optimization process. The Pareto set suggested that an airflow organization with dual circulation may be optimal.
Improving Software Defect Prediction in Noisy Imbalanced Datasets
Authors: Shi Haoxiang , Ai Jun , Liu Jingyu , Xu Jiaxi
Published: September 2023

Software defect prediction is a popular method for optimizing software testing and improving software quality and reliability. However, software defect datasets usually have quality problems, such as class imbalance and data noise. Oversampling by generating the minority class samples is one of the most well-known methods to improving the quality of datasets; however, it often introduces overfitting noise to datasets. To better improve the quality of these datasets, this paper proposes a method called US-PONR, which uses undersampling to remove duplicate samples from version iterations and then uses oversampling through propensity score matching to reduce class imbalance and noise samples in datasets. The effectiveness of this method was validated in a software prediction experiment that involved 24 versions of software data in 11 projects from PROMISE in noisy environments that varied from 0% to 30% noise level. The experiments showed a significant improvement in the quality of datasets pre-processed by US-PONR in noisy imbalanced datasets, especially the noisiest ones, compared with 12 other advanced dataset processing methods. The experiments also demonstrated that the US-PONR method can effectively identify the label noise samples and remove them.
Antioxidant Potential of Yogurts Produced from Milk of Cows Fed Fodder Supplemented with Herbal Mixture with Regard to Refrigerated Storage
Authors: Stobiecka Magdalena , Król Jolanta , Brodziak Aneta
Published: September 2023

The aim of the study was to assess the potential of milk from herbal blend-fed cows to be used for the production of yogurts exhibiting increased antioxidant potential with regard to the duration of refrigerated storage of the products. Bulk milk (control—CM and experimental—EM) intended for the production of yogurts was provided by a dairy cattle breeding farm. The milk samples were analyzed to determine their basic chemical composition (the content of dry matter, fat, and total protein including casein), hygienic status (somatic cell count (SCC) and total microbial count (TMC)), and antioxidant activity (FRAP, DPPH, and ABTS assays). Pasteurized milk was used to manufacture natural yogurts with the use of starter cultures YC-X11 (Chr. Hansen, Hørsholm, Denmark). Changes in physicochemical traits (acidity, nutritional value, and water activity) and antioxidant activity (FRAP, DPPH, and ABTS assays) occurring during 21-day refrigerated storage of the yogurts were determined. The analyses revealed that the yogurts had higher antioxidant potential than the milk, irrespective of the determination method. Additionally, the experimental yogurts produced from milk obtained from the cows fed fodder supplemented with an herbal mixture exhibited significantly higher antioxidant activity than the control yogurts. The antioxidant potential of the yogurts changed during the refrigerated storage. It should be emphasized that their antioxidant activity significantly increased during the first two weeks (until day 14) but decreased by 15–20% in the following week.
Diagnosing Hurricane Barry Track Errors and Evaluating Physics Scalability in the UFS Short-Range Weather Application
Authors: Lybarger Nicholas D. , Newman Kathryn M. , Kalina Evan A.
Published: September 2023

To assess the performance and scalability of the Unified Forecast System (UFS) Short-Range Weather (SRW) application, case studies are chosen to cover a wide variety of forecast applications. Here, model forecasts of Hurricane Barry (July 2019) are examined and analyzed. Several versions of the Global Forecast System (GFS) and Rapid Refresh Forecast System (RRFS) physics suites are run in the UFS-SRW at grid spacings of 25 km, 13 km, and 3 km. All model configurations produce significant track errors of up to 350 km at landfall. The track errors are investigated, and several commonalities are seen between model configurations. A westerly bias in the environmental steering flow surrounding the tropical cyclone (TC) is seen across forecasts, and this bias is coincident with a warm sea surface temperature (SST) bias and overactive convection on the eastern side of the forecasted TC. Positive feedback between the surface winds, latent heating, moisture, convection, and TC intensification is initiated by this SST bias. The asymmetric divergent flow induced by the excess convection results in all model TC tracks being diverted to the east as compared to the track derived from reanalysis. The large differences between runs using the same physics packages at different grid spacing suggest a deficiency in the scalability of these packages with respect to hurricane forecasting in vertical wind shear.
Investigating the Production Atmosphere for Sulfide-Based Electrolyte Layers Regarding Occupational Health and Safety
Authors: Kreher Tina , Jäger Patrick , Heim Fabian , Birke Kai
Published: September 2023

In all-solid-state battery (ASSB) research, the importance of sulfide electrolytes is steadily increasing. However, several challenges arise concerning the future mass production of this class of electrolytes. Among others, the high reactivity with atmospheric moisture forming toxic and corrosive hydrogen sulfide (H2S) is a major issue. On a production scale, excessive exposure to H2S leads to serious damage of production workers’ health, so additional occupational health and safety measures are required. This paper investigates the environmental conditions for the commercial fabrication of slurry-based sulfide solid electrolyte layers made of Li3PS4 (LPS) and Li10GeP2S12 (LGPS) for ASSBs. First, the identification of sequential production steps and processing stages in electrolyte layer production is carried out. An experimental setup is used to determine the H2S release of intermediates under different atmospheric conditions in the production chain, representative for the production steps. The H2S release rates obtained on a laboratory scale are then scaled up to mass production dimensions and compared to occupational health and safety limits for protection against H2S. It is shown that, under the assumptions made for the production of a slurry-based electrolyte layer with LPS or LGPS, a dry room with a dew point of τ=−40 ∘C and an air exchange rate of AER=30 1h is sufficient to protect production workers from health hazards caused by H2S. However, the synthesis of electrolytes requires an inert gas atmosphere, as the H2S release rates are much higher compared to layer production.
Efficient and Controllable Model Compression through Sequential Knowledge Distillation and Pruning
Authors: Malihi Leila , Heidemann Gunther
Published: September 2023

Efficient model deployment is a key focus in deep learning. This has led to the exploration of methods such as knowledge distillation and network pruning to compress models and increase their performance. In this study, we investigate the potential synergy between knowledge distillation and network pruning to achieve optimal model efficiency and improved generalization. We introduce an innovative framework for model compression that combines knowledge distillation, pruning, and fine-tuning to achieve enhanced compression while providing control over the degree of compactness. Our research is conducted on popular datasets, CIFAR-10 and CIFAR-100, employing diverse model architectures, including ResNet, DenseNet, and EfficientNet. We could calibrate the amount of compression achieved. This allows us to produce models with different degrees of compression while still being just as accurate, or even better. Notably, we demonstrate its efficacy by producing two compressed variants of ResNet 101: ResNet 50 and ResNet 18. Our results reveal intriguing findings. In most cases, the pruned and distilled student models exhibit comparable or superior accuracy to the distilled student models while utilizing significantly fewer parameters.