Miracle-dz/UniFAR-FTUs huggingface dataset:
Analyzing the behavior toward Tempeh waste management at the home-scale industry level in Tempeh Village Sukomanunggal Surabaya

Tempeh is a daily food, especially for Indonesians. Many people consume it as a source of protein for their staple food. However, people rarely know how Tempeh is produced in Indonesia. Most Tempeh is made by the home-scale industry, which still lacks in environmental awareness. Therefore, many Tempeh industries still directly dispose of their waste, causing environmental pollution in neighborhood areas. This study aimed to analyze the behaviors of the owners of the Tempeh industry concerning waste disposal or treatment. The respondents in this study were the owners of the home-scale Tempeh industry located in Tempeh Village Sukomanunggal Surabaya City. The collected data were processed using the structural equation model partial least square (SEM PLS) to determine the factors influencing the behavior toward waste disposal or waste treatment. The results showed various factors affecting the owners in disposing or treating waste from Tempeh production. These were Attitude Toward Behavior (ATB), Subjective Norm (SN), Perceived Behavioral Control (PBC), Environmental Awareness (EA), Government Intervention (GI), and Knowledge (KN), which affect the Intention to Manage Tempeh Waste Generation (IMTWP). The findings confirmed that the goal to treat Tempeh waste enhances the owners’ behaviors to manage the disposal or treatment of the waste from Tempeh Village in Sukomanunggal - Surabaya.
PRODUCTIVE AND PHYSIOLOGICAL EFFECTS OF PROBIOTICS AND APPLE VINEGAR ON BROILER CHICKENS UNDER HIGH STOCKING DENSITY

: This study aimed to assess the effect of watery supplementation of probiotic and/or apple vinegar on productive and physiological performances of broiler chickens (Ross 308) reared under high stocking density. Three hundred thirty-six unsexed one-day-old broiler chicks were randomly distributed to eight different treatments (3 replicates each) as follow: T 1 : Negative control (low stocking density (LSD) 12 birds/m 2 ; basal diet with no studied additives in drinking water), T 2 : LSD + probiotic, T 3 : LSD + apple vinegar, T 4 : LSD + probiotics + apple vinegar, T 5 : Positive control (high stocking density (HSD), 16 birds/m 2 ; basal diet with no studied additives in drinking water), T 6 : HSD + Probiotics, T7: HSD + apple vinegar and T 8 : HSD + probiotics+ apple vinegar. Probiotic mixture or/and apple vinegar were provided at a dose of 0.5% in drinking water. The body weight (BW), body weight gain (BWG), feed intake (FI) and feed conversion ratio (FCR) were measured every growth phase until the end of the study at 35 day of age. Also 6 birds of each treatment were used to evaluate carcass traits. Results revealed that administration of probiotics individually or in combined with apple vinegar significantly increased BW, BWG and FI, with a decreased FCR at grower (21 days of age) phase and market age (35 days of age), while apple vinegar additive groups had the lowest BW, BWG and FI, with increased FCR in both tested stocking density. In addition, enhanced carcass yield, some carcass parts (breast and thigh), blood constituents and beneficial bacteria count in small intestine of broilers provided by additives suggested. In conclusion, this study proves valuable effects of probiotic and apple vinegar on growth performance, carcass traits, and microbiological measurements at grower and finisher phases of broilers raised under high stocking density.
Library occupancy warning system based on dual-channel detection

This paper proposes a dual-channel detection model based on Faster-RCNN object detection algorithm and objects classification algorithm, which aims to detect the phenomenon of seat occupation in university libraries, provide accurate positioning for librarians, and improve the utilization rate of seats. The data set is constructed by combining network acquisition with UE5 virtual reality construction. The dual-channel detection comprises the following two steps. In first step, a target detection algorithm is used to judge whether a person is on the seat. Next, the objects classification algorithm is used to classify and identify the pictures without people to judge whether the person is suspected of occupying the seat. The research uses deep learning method to solve the problem of seat occupation in library seat system, effectively improves the detection accuracy of seat occupation recognition, and greatly improves the management efficiency of library seats.
Mesh-Free Simulation of Spatially Inhomogeneous Aerosols in Arbitrary Geometries

Abstract While many issues affect the composition and quantity of the nuclear source term, one significant factor is the existence of aerosols. These aerosols, found in the containment structure and in the primary reactor vessel, are usually simulated with the assumption that they are spatially homogeneous. We describe here new investigations of the applications of the Direct Simulation Monte Carlo method and a mesh-free technique to spatially inhomogeneous aerosol evolution in a number of nonspherical and complex geometries. Deposition, coagulation, and condensation aerosol processes are included, and results are reported for a sphere, ellipsoid, torus, elliptical cylinder, cuboid, and a spherical geometry containing an internal obstruction. Our progress here is a precursor to construction of an MCNP-like code for simulating aerosol evolution.
Fault Characteristic Analysis and a Novel Pilot Protection Scheme for Pole-to-Ground Fault in Flexible DC Distribution Network

Due to the pseudo-bipolar structure of the flexible DC distribution network, the short-circuit current caused by a pole-to-ground fault is weak. The fault characteristics, particularly the steady-state fault characteristics, are not prominent. Therefore, pole-to-ground fault diagnosis and protection have become more challenging. This paper theoretically studies the short-circuit current characteristics of pole-to-ground fault in the flexible DC distribution network, derives the analytical expression of the short-circuit current, and analyzes the directional characteristics. Then, a novel pilot protection scheme based on fault direction comparison is proposed. This scheme compares the positive and negative half-wave integral values of the short-circuit current fault components to judge the fault direction. The fault directions on both sides of the feeder are obtained through the pilot protection channel, thereby distinguishing internal and external faults. The simulation results show that the proposed scheme can reliably identify the faulty feeder and has an excellent ability to perform with high transition resistance. Moreover, the scheme does not require sampling synchronization, which is easy to be applied in engineering.
Command filtered backstepping‐based containment control for a class of multi‐agent systems with uncertainty

In this paper, a novel containment control scheme is designed for multi‐agent systems with unknown parameters and unknown control coefficients. To achieve the containment control goal, containment reference signals are generated using a certain number of dynamic leaders. Then, the followers directly receiving the reference signals can track their generated reference signals, and the rest of the followers track a certain number of estimated signals generated by designed dynamic equations. Under the backstepping framework, uncertainties of a multi‐agent system are compensated for through the adaptive bound estimation method. Additionally, the command filtered technique is adopted to avoid the cumbersome steps of multiple derivative calculations, and to simplify the operations of the control scheme. Finally, the stability of the closed‐loop multi‐agent system is proved based on the Lyapunov theory. The advantage of the proposed scheme is that the containment reference signal and the position between followers can be adjusted arbitrarily by adjusting the relevant parameters. Through simulations, the effectiveness and superiority of the designed controller are verified.
High-quality initial shape estimation for cascade shape regression

Cascade shape regression has been proven to be an accurate, robust and fast framework for face alignment. Recently, a lot of methods based on this framework have emerged which focus on boosting learning method or extracting geometric invariant features. Despite the great success of these methods, none of them are initialization independent, which limits their prediction performance to some complex face shapes. In this paper, we propose a novel initialization scheme called high-quality initial shape estimation to generate high-quality initial face shapes. First, we extract Gabor features to represent facial appearance. Then we minimize the square error between the target shapes and the estimated initial shapes using a random regression forest and binary comparison features. Finally, we use a standard cascade shape regressor to regress the estimated initial shape for robust face alignment. Experimental results show that our method achieves state-of-the-art performance on the 300-W dataset, which is the most challenging dataset today.
Investigating the Effect of Body Composition Differences on Seismocardiogram Characteristics

In seismocardiogram (SCG) analysis, inter-subject variability is observed as the medium between the heart and accelerometer consists of different tissues made of bone, muscle, fat and skin cells of which combination varies across different people. Anatomically, a similar pattern is present in the speech production system, where the vocal cord and vocal tract are considered as the source and medium, respectively. For observing the change of the vocal tract filter while voicing different sounds, linear predictive analysis has been used for years. Thus, it was hypothesized that the medium characteristics of the human thorax would also have a filtering effect on the SCG signals and the differences in the filtering effects would be observed in the respiration (<1 Hz), vibration (1–20 Hz) and acoustic (>20 Hz) characteristics of the SCG signals. To that aim, three different binary classification tasks representing the body composition differences were defined: (i) whether the metabolic age of the subject is more than the real age of the subject, (ii) whether the BMI of the subject is bigger than 25, and (iii) whether the subject is male or female. To understand the metabolism-induced changes in the respiration, vibration and acoustic components, classification experiments were conducted using different frequency bands of the SCG signal. In each case, linear predictive coefficients were extracted and used to train individual classification models for the aforementioned scenarios. With the vibration components (1–20 Hz), all of the tasks resulted in high performance (0.86, 0.93, 0.93) for age, BMI and gender classification tasks, respectively. This study reveals that the vibration components of SCG make a stable and informative contribution to selected classification tasks, and due to its high generalizability, it is suitable for various practical applications.
Acceptability of risk-tailored cancer screening among Australian GPs: a qualitative study

Background Cancer screening that is tailored to individual risk has the potential to improve health outcomes and reduce screening-related harms, if implemented well. However, successful implementation depends on acceptability, particularly as this approach will require GPs to change their practice. Aim To explore Australian GPs’ views about the acceptability of risk-tailored screening across cancer types and to identify barriers to and facilitators of implementation. Design and setting A qualitative study using semi-structured interviews with Australian GPs. Method Interviews were carried out with GPs and audio-recorded and transcribed. Data were first analysed inductively then deductively using an implementation framework. Results Participants (n = 20) found risk-tailored screening to be acceptable in principle, recognising potential benefits in offering enhanced screening to those at highest risk. However, they had significant concerns that changes in screening advice could potentially cause confusion. They also reported that a reduced screening frequency or exclusion from a screening programme for those deemed low risk may not initially be acceptable, especially for common cancers with minimally invasive screening. Other reservations about implementing risk-tailored screening in general practice included a lack of high-quality evidence of benefit, fear of missing the signs or symptoms of a patient’s cancer, and inadequate time with patients. While no single preferred approach to professional education was identified, education around communicating screening results and risk stratification was considered important. Conclusion GPs may not currently be convinced of the net benefits of risk-tailored screening. Development of accessible evidence-based guidelines, professional education, risk calculators, and targeted public messages will increase its feasibility in general practice.
Explainable machine learning for phishing feature detection

Phishing is a very dangerous security threat that affects individuals as well as companies and organizations. To fight the risks associated with this threat, it is important to detect phishing websites in a timely manner. Machine learning models work well for this purpose as they can predict phishing cases, using information on the underlying websites. In this paper, we contribute to the research on the detection of phishing websites by proposing an explainable machine learning model that can provide not only accurate predictions of phishing, but also explanations of which features are most likely associated with phishing websites. To this aim, we propose a novel feature selection model based on Lorenz Zonoids, the multidimensional extension of Gini coefficient. We illustrate our proposal on a real dataset that contains features of both phishing and legitimate websites.
Mining Quasi-Frequent Subnetworks in Graph Networks Using Edge-Edge Summary Graph

In today’s computing world, graphs have become increasingly important in modeling sophisticated structures, entities and their interactions, with broad applications including Bioinformatics, Computer Vision, Web analysis etc. For an example, multiple gene expressions samples over the same set of genes are recorded to strengthen the evidence of co-expression patterns. These can be modeled by forming a set of graphs for these samples. The problem is how to dig into such multiple sources of information to make better inferences. In this paper, I have presented an efficient method to find useful subnetworks from graph networks. The idea is to create a summary graph from these networks and then find subnetworks using this graph. I have given a detailed comparison between an already existing approach called vertex-vertex summary graph approach and the approach discussed in this paper. The results I have found are more promising than for the existing approach.
RevI-Ve: A Comprehensive Software Interface for Easy ReRAM Device Characterization

Introducing the uninitiated students & researchers to ReRAM technology and its applications via true device characterization and practical laboratory work, will certainly motivate them further to implement and test their application ideas in hardware. However, the lack of knowledge of the necessary experiments and strategies to follow for the adequate treatment of such devices, is a limiting factor in this direction. This work bridges the gap introducing RevI-Ve, a comprehensive characterization software interface developed for laboratory work on ReRAM devices. RevI-Ve is compatible with the Digilent Analog Discovery instrument series and is suitable both for educational and for research purposes. RevIVe supports different voltage-/current-based circuit topologies to access the ReRAM devices and enables the quick realization of a variety of complex, yet fully-customizable, experiments. We present the principal advantages offered by the current version of this tool, a brief summary of the experiments it facilitates, and show some results with data collected from measurements on commercial Self-Directed-Channel devices sold by Knowm Inc.
The ultra-long GRB 220627A at z=3:08

GRB 220627A is a rare burst with two distinct gamma-ray emission episodes separated by almost 1000 s that triggered the Fermi Gamma-ray Burst Monitor twice. High-energy GeV emission was detected by the Fermi Large Area Telescope coincident with the first emission episode but not the second. The discovery of the optical afterglow with MeerLICHT led to MUSE observations which secured the burst redshift to z=3.08, making this the most distant ultra-long gamma-ray burst (GRB) detected to date. The progenitors of some ultra-long GRBs have been suggested in the literature to be different to those of normal long GRBs. Our aim is to determine whether the afterglow and host properties of GRB 220627A agree with this interpretation. We performed empirical and theoretical modelling of the afterglow data within the external forward shock framework, and determined the metallicity of the GRB environment through modelling the absorption lines in the MUSE spectrum. Our optical data show evidence for a jet break in the light curve at ~1.2 days, while our theoretical modelling shows a preference for a homogeneous circumburst medium. Our forward shock parameters are typical for the wider GRB population, and we find that the environment of the burst is characterised by a sub-solar metallicity. Our observations and modelling of GRB 220627A do not suggest that a different progenitor compared to the progenitor of normal long GRBs is required. We find that more observations of ultra-long GRBs are needed to determine if they form a separate population with distinct prompt and afterglow features, and possibly distinct progenitors.
SMT Beyond DPLL(T): A New Approach to Theory Solvers and Theory Combination

Satisifiability modulo theories (SMT) is the problem of deciding whether a given logical formula can be satisifed with respect to a combination of background theories. The past few decades have seen many significant developments in the field, including fast Boolean satisfiability solvers (SMT), efficient decision procedures for a growing number of expressive theories, and frameworks for modular combination of decision procedures. All these improvements, with addition of robust SMT solver implementations, culminated with the acceptance of SMT as a standard tool in the fields of automated reasoning and computer added verification. In this thesis we develop new decision procedures for the theory of linear integer arithmetic and the theory of non-linear real arithmetic, and develop a new general framework for combination of decision procedures. The new decision procedures integrate theory specific reasoning and the Boolean search to provide a more powerful and efficient procedures, and allow a more expressive language for explaining problematic states. The new framework for combination of decision procedures overcomes the complexity limitations and restrictions on the theories imposed by the standard Nelson-Oppen approach.
Histopathological Images Analysis and Predictive Modeling Implemented in Digital Pathology—Current Affairs and Perspectives

In modern clinical practice, digital pathology has an essential role, being a technological necessity for the activity in the pathological anatomy laboratories. The development of information technology has majorly facilitated the management of digital images and their sharing for clinical use; the methods to analyze digital histopathological images, based on artificial intelligence techniques and specific models, quantify the required information with significantly higher consistency and precision compared to that provided by optical microscopy. In parallel, the unprecedented advances in machine learning facilitate, through the synergy of artificial intelligence and digital pathology, the possibility of diagnosis based on image analysis, previously limited only to certain specialties. Therefore, the integration of digital images into the study of pathology, combined with advanced algorithms and computer-assisted diagnostic techniques, extends the boundaries of the pathologist’s vision beyond the microscopic image and allows the specialist to use and integrate his knowledge and experience adequately. We conducted a search in PubMed on the topic of digital pathology and its applications, to quantify the current state of knowledge. We found that computer-aided image analysis has a superior potential to identify, extract and quantify features in more detail compared to the human pathologist’s evaluating possibilities; it performs tasks that exceed its manual capacity, and can produce new diagnostic algorithms and prediction models applicable in translational research that are able to identify new characteristics of diseases based on changes at the cellular and molecular level.
Development of an ultra-wideband, stable and high-directive monopole disc antenna for radar-based microwave imaging of breast cancer

ABSTRACT Radar-based ultra-wideband (UWB) microwave imaging (MI) system has recently attracted considerable attention for detection of breast cancer at early stage. A key component of the system is the sensor (antenna) that is used to radiate and receive the UWB pulses. In this study, a compact-size, UWB, directive, and microstrip-fed planar circular disc monopole antenna (PCDMA) with modified L-shaped ground plane for the radar-based UWB MI system is developed. Analysis and the performance optimization of the proposed antenna are performed by HFSS software. In the development process of the antenna design, some modifications such as adjusting feed point, adding parasitic element, slot, notches are made on the conventional PCDMA to improve the bandwidth, directivity and half-power beamwidth (HPBW) of the antenna. After obtaining the optimum parameters, the proposed antenna is fabricated and then tested by using Vector Network Analyser. Impedance bandwidth of the designed antenna covers the broad frequency range of 3–10 GHz (108%) for Return Loss < –10 dB. The good stability of the directional radiation patterns is obtained in the 3–10 GHz. The direction of the maximum radiation moves from 32° to 54° and HPBW decreases from 56° to 30° through the frequency band. The directivity changes between 6.2 and 9.4 dB in the 3–10 GHz. Results confirm that the proposed antenna is suitable for using in radar-based UWB MI of the breast cancer.
Improved antireflection coated microspheres for biological applications of optical tweezers

The success of optical tweezers in cellular biology1 is in part due to the wide range of forces that can be applied, from femto- to hundreds of pico-Newtons; nevertheless extending the range of applicable forces to the nanoNewton regime opens access to a new set of phenomena that currently lie beyond optical manipulation. A successful approach to overcome the conventional limits on trapping forces involves the optimization of the trapped probes. Jannasch et al.2 demonstrated that an anti-reflective shell of nanoporous titanium dioxide (aTiO2, nshell = 1.75) on a core particle made out of titanium dioxide in the anatase phase (cTiO2, ncore = 2.3) results in trappable microspheres capable to reach forces above 1 nN. Here we present how the technique can be further improved by coating the high refractive index microspheres with an additional anti-reflective shell made out of silica (SiO2). This external shell not only improves the trap stability for microspheres of different sizes, but also enables the use of functionalization techniques already established for commercial silica beads in biological experiments. We are also investigating the use of these new microspheres as probes to measure adhesion forces between intercellular adhesion molecule 1 (ICAM-1) and lymphocyte function-associated antigen 1 (LFA-1) in effector T-Cells and will present preliminary results comparing standard and high-index beads.