Miracle-dz/UniFAR-FTUs huggingface dataset:
Teaching Gender Differences at Medical School Could Improve the Safety and Efficacy of Personalized Physical Activity Prescription

The most recent scientific evidence on gender medicine indicates that the teaching of gender medicine is fundamental in the training of doctors and health professionals. This opinion paper aims to focus attention on the benefits of systematic teaching of gender medicine to improve the safety and efficacy of prescribing personalized physical activity and sport. The practice of physical activity and sport has become extremely widespread in the elderly adult population also following the indications of the WHO Global Action Plan on Physical Activity 2018–2030 which indicates the prescription of physical activity for the prevention of non-communicable diseases (1). A recent manuscript points out that implicit prejudice education is effective in raising awareness of prejudice regardless of personal beliefs (2). Raising awareness of bias is one of many steps toward creating a positive learning environment and a more equitable healthcare system (2).
Identification of Pyroptosis-Related Gene Signatures and Construction of the Risk Model to Predict BCR in Prostate Cancer

Prostate cancer is one of the most common malignant tumors in men. Pyroptosis is related to tumor immune infiltration and tumor microenvironment (TME) and has been confirmed to be related to the progression of a variety of tumors. However, the relationship between prostate cancer and pyroptosis, as well as TME and tumor immune infiltration, has not been discussed yet. We obtained and combined the RNA-seq data of prostate cancer from TCGA and GEO databases, analyzed the differential expression of pyroptosis-related genes (PRGs), and divided them into two groups according to the PRG expression level. The relationship between pyroptosis subtypes and the TME of prostate cancer was further verified, and the differential expression genes (DEGs) in the two subtypes were identified. The relationship between the DEGs and clinicopathology was explored and KEGG and GO enrichment analysis was conducted; it was found that most DEGs were enriched in immune-related pathways. Then, we randomly divided datasets into training and testing sets, performed the LASSO and multicox progression analysis, selected eight genes as prognostic signatures and used the eight genes, calculated the risk score, and then separated the entire cohort into high- and low-risk groups. The prognosis between two groups and the 1-, 3-, and 5-year ROC curves of biochemical relapse (BCR) were verified in training, testing, and the entire cohort, respectively. The TME, CSC index, mutation, and drug susceptibility were also discussed.
Augmented intelligence for state-of-the-art patent search

The volume of patent data is increasing, which is a big challenge to patent examiners as well as to all inventive companies and individuals. In this paper we take the view of individual inventors who believe they invented something new. Artificial intelligence brings a promise to support their prior art search for existing (similar) inventions with machine learning and deep learning algorithms. We discuss the potential of artificial intelligence in prior art searching. We present an experiment, based on a real-life invention, comparing relevant patents we got from Boolean keyword searching with those from the semantic search supported by artificial intelligence. We can confirm that artificial intelligence has great potential in this field. However, presently it is not yet able to make traditional patent search engines obsolete, hence it still fits better with the notions of augmented intelligence or expertise.
Using a Person-Centered Approach to Facilitate a Male Amateur Distance Runner’s Personal Growth

This case study analyzes, synthesizes, and tests a person-centered approach by a trainee sport and exercise psychologist working with a male amateur distance runner. The aim of applying a person-centered approach was to enhance the athlete’s understanding of himself, enable him to make sense of his experience, and facilitate personal growth. This case study explores the client’s change process and how developing the quality of the relationship allowed him to share his true thoughts and feelings and move towards his authentic self. The trainee describes the theoretical framework that guided the consultancy; reflects on the experiences of exhibiting empathy, unconditional positive regard, and congruence; and discusses the challenges with implementing the approach. The case study reflects on and evaluates the support service, so that the practitioner could learn from this experience and enhance service delivery as he moves toward the end of his formal training.
Development of Enumerative and Binomial Sequential Sampling Plans for Monitoring Lymantria dispar (L.) (Lepidoptera Erebidae) in Mediterranean Oak Forests

Lymantria dispar is the main threat to Mediterranean forests. Sampling methods used for monitoring the pest population density are generally very time-consuming for practical purposes, such as the delimitation of infested areas for control programs. Enumerative and binomial sequential sampling plans were developed using data collected in cork oak forests in Sardinia (Italy). The Taylor’s power law (TPL) was used to evaluate the degree of aggregation of L. dispar egg masses among trees and to develop enumerative sampling plans at precision levels of 0.10 and 0.25 using the Green’s method. Furthermore, binomial plans were computed by Wald’s sequential probability ratio test. Lymantria dispar egg masses on trees were significantly aggregated and the degree of aggregation was similar in all population development phases. Overall, only 31 cork oak trees are to be monitored at the economic damage threshold of 2.5 egg masses/tree with a precision level of 0.25. Binomial sequential sampling plans also required lower sampling sizes (26.9–31.4 trees) than conventional sampling plans. Enumerative and binomial sampling plans could represent suitable methods for sampling L. dispar egg masses in Mediterranean forests, with the practical advantage of lower cost and time consumption than standard sampling plans.
Deep Learning-Based Trajectory Planning and Control for Autonomous Ground Vehicle Parking Maneuver

In this paper, a novel integrated real-time trajectory planning and tracking control framework capable of dealing with autonomous ground vehicle (AGV) parking maneuver problems is presented. In the motion planning component, a newly-proposed idea of utilizing deep neural networks (DNNs) for approximating optimal parking trajectories is further extended by taking advantages of a recurrent network structure. The main aim is to fully exploit the inherent relationships between different vehicle states in the training process. Furthermore, two transfer learning strategies are applied such that the developed motion planner can be adapted to suit various AGVs. In order to follow the planned maneuver trajectory, an adaptive learning tracking control algorithm is designed and served as the motion controller. By adapting the network parameters, the stability of the proposed control scheme, along with the convergence of tracking errors, can be theoretically guaranteed. In order to validate the effectiveness and emphasize key features of our proposal, a number of experimental studies and comparative analysis were executed. The obtained results reveal that the proposed strategy can enable the AGV to fulfill the parking mission with enhanced motion planning and control performance. Note to Practitioners—This article was motivated by the problem of optimal automatic parking planning and tracking control for autonomous ground vehicles (AGVs) maneuvering in a restricted environment (e.g., constrained parking regions). A number of challenges may arise when dealing with this problem (e.g., the model uncertainties involved in the vehicle dynamics, system variable limits, and the presence of external disturbances). Existing approaches to address such a problem usually exploit the merit of optimization-based planning/control techniques such as model predictive control and dynamic programming in order for an optimal solution. However, two practical issues may require further considerations: 1). The nonlinear (re)optimization process tends to consume a large amount of computing power and it might not be affordable in real-time; 2). Existing motion planning and control algorithms might not be easily adapted to suit various types of AGVs. To overcome the aforementioned issues, we present an idea of utilizing the recurrent deep neural network (RDNN) for planning optimal parking maneuver trajectories and an adaptive learning NN-based (ALNN) control scheme for robust trajectory tracking. In addition, by introducing two transfer learning strategies, the proposed RDNN motion planner can be adapted to suit different AGVs. In our follow-up research, we will explore the possibility of extending the developed methodology for large-scale AGV parking systems collaboratively operating in a more complex cluttered environment.
Waterproof Design of Soft Multi-Directional Force Sensor for Underwater Robotic Applications

Directional force sensing is an intrinsic feature of tactile sensing. As technologies of exploratory robots evolve, with special emphasis on the emergence of soft robotics, it is crucial to equip robotic end-effectors with effective means of characterizing trends in force detection and grasping phenomena, while these trends are largely derived from networks of tactile sensors working together, individual sensors must be built to meet an intended function and maintain functionality with respect to environmental operating conditions. The harshness of underwater exploration imposes a unique set of circumstances onto the design of tactile sensors. When exposed to underwater conditions a tactile sensor must be able to withstand the effects of increased pressure paired with water intrusion while maintaining computational and mechanical integrity. Robotic systems designed for the underwater environment often become expensive and cumbersome. This paper presents the design, fabrication, and performance of a low-cost, soft-material sensor capable of multi-directional force detection. The fundamental design consists of four piezo-resistive flex elements offset at 90∘ increments and encased inside of a hemispherical silicone membrane filled with a non-compressive and non-conductive fluid. The sensor is simulated numerically to characterize soft-material deformation and is experimentally interrogated with indentation equipment to investigate sensor-data patterns when subject to different contact forces. Furthermore, the sensor is subject to a cyclic loading test to analyze the effects of hysteresis in the silicone and is submerged underwater for a 7-day period to investigate any effect of water intrusion at a shallow depth. The outcome of this paper is the proposed design of a waterproofed, soft-material tactile sensor capable of directional force detection and contact force localization. The overall goal is to widen the scope of tactile sensor concepts outfitted for the underwater environment.
Effect of Traditional Stir-Frying on the Characteristics and Quality of Mutton Sao Zi

The effects of stir-frying stage and time on the formation of Maillard reaction products (MRP) and potentially hazardous substances with time in stir-fried mutton sao zi were investigated. Furosine, fluorescence intensity, Nε-(1-carboxymethyl)-L-lysine (CML), Nε-(1-carboxyethyl)-L-lysine (CEL), polyaromatic hydrocarbons PAHs), heterocyclic aromatic amines (HAAs), and acrylamides (AA) mainly presented were of stir-fried mutton sao zi. The furosine decreased after mixed stir-frying (MSF) 160 s due to its degradation as the Maillard reaction (MR) progressed. The fluorescent compound gradually increased with time during the stir-frying process. The CML and CEL peaked in MSF at 200 s. AA reached its maximum at MSF 120 s and then decreased. All the 5 HAAs were detected after MSF 200 s, suggesting that stir-frying mutton sao zi was at its best before MSF for 200 s. When stir-frying exceeded the optimal processing time of (MSF 160 s) 200 s, the benzo[a]pyrene peaked at 0.82 μg/kg, far lower than the maximum permissible value specified by the Commission of the European Communities. Extended stir-frying promoted MRP and some hazardous substances, but the content of potentially hazardous substances was still within the safety range for food.
Forming a transparent atmospheric optical channel by use of vortical laser beam

It was shown that promising way to reduce a power loss of optical waves transmitted through atmosphere is use of spatially inhomogeneous optical waves with phase front in form of flat spiral. Vortical optical beam used to separate aerosol (due to light pressure on water particles) and create a cone wherein droplets concentration is substantially reduced. Unlike known method to create a transparent atmospheric optical channel based on use evaporation of aerosol particles via high‐power laser radiation, proposed method is substantially less energy‐consuming since when moving aqueous aerosol particle from area of laser beam propagation no evaporation occurs.
Fatty Acids as a Tool to Boost Cancer Immunotherapy Efficacy

Although immunotherapy represents one of the most potent therapeutic anti-cancer approaches, only a limited number of patients shows clinical benefit. Recent evidence suggests that patients' nutritional status plays a major role in immunotherapy outcome. Fatty acids are essential in a balanced diet and well-known to influence the immune response. Moreover, short-chain fatty acids (SCFAs) show beneficial effects in metabolic disorders as well as in cancer and polyunsaturated fatty acids (PUFAs) contribute to body weight and fat free mass preservation in cancer patients. In line with these data, several studies imply a role for SCFAs and PUFAs in boosting the outcome of immunotherapy. In this review, we specifically focus on mechanistic data showing that SCFAs modulate the immunogenicity of tumor cells and we discuss the direct effects of SCFAs and PUFAs on the immune system in the context of cancer. We provide preclinical and clinical evidence indicating that SCFAs and PUFAs may have the potential to boost immunotherapy efficacy. Finally, we describe the challenges and address opportunities for successful application of nutritional interventions focusing on SCFAs and PUFAs to increase the therapeutic potential of immunotherapeutic approaches for cancer.
Analisis Resiko Teknologi Informasi Sistem Terintegrasi iGracias Berbasis Risk Assesment Menggunakan SNI ISO-IEC 27001-2009

Sistem terintegrasi merupakan tantangan menarik dalam software development karena proses pengembangannya harus mengacu pada konsistensi sistem, agar sub-sub sistem yang sudah ada secara operasional masih tetap berfungsi sebagaimana mestinya, baik ketika proses mengintegrasikan sistem maupun setelah terintegrasi [1]. Universitas Telkom memanfaatkan fasilitas e-Learning untuk membantu proses pembelajaran dalam sistem terintegrasi. Aplikasi e-Learning Universitas Telkom bernama IDEA (Integrated Distance Education Application) dan pengelolaan aktivitas manajemen di dalam sistem tersebut, saat ini dapat diakses melalui portal sistem informasi Universitas Telkom, iGracias (Telkom University Integrated Information System). Namun, tidak dapat dipungkiri bahwa kemungkinan munculnya berbagai ancaman dan risiko dapat menghambat bahkan melumpuhkan aktivitas di dalam sistem, salah satunya disebabkan oleh teknologi informasi yang digunakan. Proses analisis ini akan menghasilkan hasil analisis resiko mengenai aset fisik beserta kemungkinan risiko yang muncul Berbasis Risk Management menggunakan SNI ISO-IEC 27001-2009 pada sistem terintegrasi iGracias. Pada akhirnya organisasi dapat melakukan pencegahan, penanganan serta perbaikan untuk ke depannya sesuai dengan tingkat prioritas risiko.
Elucidating the Cognitive Mechanisms Underpinning Behavioural Activation

Abstract. Depression represents a pervasive and devastating psychological diﬃculty. It aﬀects over 21% of the population at some point in their lives and can have an unimaginable impact on both individuals and society. Behavioural activation represents a popular intervention for depression and is commonly used by psychologists internationally. Despite its popularity, the cognitive mechanisms underpinning the eﬃcacy of behavioural activation remain elusive. This paper will review the literature on this intervention and propose an account of the cognitive mechanisms underlying its therapeutic eﬃcacy. Speciﬁcally, it is argued that behavioural activation is eﬀective because it increases both the density of outcomes in ones environment and the density of highly salient action-outcome contingencies, which may otherwise be absent due to reduced motivation. The clinical implications are subsequently discussed, with reference to future research.
Large-Scale, High-Throughput Phenotyping of the Postharvest Storage Performance of ‘Rustenburg’ Navel Oranges and the Development of Shelf-Life Prediction Models

We conducted a large-scale, high-throughput phenotyping analysis of the effects of various pre-harvest and postharvest features on the quality of ‘Rustenburg’ navel oranges, in order to develop shelf-life prediction models to enable the use of the First Expired, First Out logistics strategy. The examined pre-harvest features included harvest time and yield, and the examined postharvest features included storage temperature, relative humidity during storage and duration of storage. All together, we evaluated 12,000 oranges (~4 tons) from six different orchards and conducted 170,576 measurements of 14 quality parameters. Storage time was found to be the most important feature affecting fruit quality, followed by storage temperature, harvest time, yield and humidity. The examined features significantly affected (p < 0.001) fruit weight loss, firmness, decay, color, peel damage, chilling injury, internal dryness, acidity, vitamin C and ethanol levels, and flavor and acceptance scores. Four regression models were evaluated for their ability to predict fruit quality based on pre-harvest and postharvest features. Extreme gradient boosting (XGBoost) combined with a duplication approach was found to be the most effective approach. It allowed for the prediction of fruit-acceptance scores among the full data set, with a root mean square error (RMSE) of 0.217 and an R2 of 0.891.
Features of Hemophagocytic Lymphohistiocytosis in Infants With Severe Combined Immunodeficiency: Our Experience From Chandigarh, North India

Background Hemophagocytic lymphohistiocytosis (HLH) is characterized by uncontrolled and excessive inflammation leading to high mortality. Aetiology of HLH can be primarily due to genetic causes or secondarily due to infections or rheumatological illness. However, rarely T-cell deficiencies like severe combined immunodeficiency (SCID) can develop HLH. Objective To describe clinical and laboratory features of SCID cases who developed HLH. Methods We collected clinical, laboratory, and molecular details of patients with SCID who developed HLH at our center at Chandigarh, North India. Results Of the 94 cases with SCID, 6 were noted to have developed HLH-like manifestations. Male-female ratio was 5:1. Median (inter-quartile range) age of onset of clinical symptoms was 4.25 months (2-5 months). Median (inter-quartile range) delay in diagnosis was 1 month (1-3.5 months). Family history of deaths was seen in 4 cases. Molecular defects in IL2RG were seen in 5 out of 6 cases. Documented infections include disseminated bacillus calmette-guerin (BCG) infection (n=2), blood stream infections (n=3) with Staphylococcal aureus (n=1), Klebsiella pneumonia (n=1), and Pseudomonas aeruginosa (n=1), pneumonia (influenza H1N1 strain, and K. pneumoniae (n=1). Conclusion Children with SCID can present with HLH-like manifestations secondary to fulminant infections. A high index of suspicion of SCID is needed in infants who present with HLH who have an associated infection or a suggestive family history. Occurrence of HLH-like manifestations in SCID suggests that T-lymphocytes may not have a significant role in immunopathogenesis of HLH.
PattGAN: Pluralistic Facial Attribute Editing

Facial attribute editing has witnessed substantial progress due to the increasing power of generative adversarial networks. However, existing methods mainly focus on improving the quality of facial attribute editing but ignore the diversity of facial attribute editing, which can only generate a single editing result by binary attribute labels and cannot reveal the diversity of attribute styles. To overcome this limitation, we propose a novel pluralistic facial attribute editing approach termed PattGAN (pluralistic attribute GAN). Instead of directly using the binary attribute labels to guide facial attribute editing, PattGAN first learns the disentangled representation of facial attributes by the binary attribute labels and then uses the disentangled representation of attributes to guide facial attribute editing. To this end, an independent encoder called the “attribute encoder” is introduced to extract distributions of specific attributes from face images. Furthermore, a novel swapping strategy is designed to assist the attribute encoder in modeling the disentangled representation of facial attributes by enhancing the model’s ability to learn pluralistic attributes. Coupled with the classification loss, the attribute encoder can accurately separate attribute-related information from face images. Both qualitative and quantitative experiments on the CelebA datasets show that PattGAN can achieve diverse face editing results by different exemplars. In summary, rather than other state-of-the-art methods, PattGAN performs better in diversity facial attribute editing.
A Multi-Authority Access Control System in Cloud Computing Using Network Security

Data access control is an efficient way to provide the data security in the cloud.and Attribute-based Encryption (ABE) technique cryptographic conducting tool to guarantee data owners direct control on their data in public cloud storage. In this paper, from another point of view, system lead an edge multipower CP-ABE access control plan for open distributed storage, named TMACS. In TMACS, exploiting (t; n) limit mystery sharing, When system verify to the user the TTP work like a SDN. Its having data controller that can evaluate the each request base on ad-hoc data table, if user is authenticated system can provide the access to specific user. Besides, by proficiently joining the customary multi-power plan with system, system build a half and half one, which fulfils the situation of traits originating from various powers and accomplishing security and framework level strength. The network security mechanism also fulfil with this approach, the system has evaluate on physical network environment with 2 to 4 physical devices and got satisfactory results than existing TMACS